Sentiment analysis, also known as opinion mining, is a natural language processing (NLP) technique used to analyze textual data. It is capable of analyzing textual data to determine whether it is positive, negative, or neutral. Businesses and brands frequently use sentiment analysis to analyze public opinion and validate brand or product sentiment via customer feedback. It also aids them in understanding the needs of their customers.
Learn what sentiment analysis is, how it works, the challenges it faces, and how to use it to improve your products, meet customer expectations and improve decision making in this post. Once you have these insights, it will be easier to get started with sentiment analysis tools that do not require coding knowledge.
What is Sentiment Analysis?
Sentiment analysis is a machine learning technique that uses NLP to identify positive and negative sentiment in text. Brands and organizations use it to detect user sentiments from feedback, measure brand reputation, and understand customers’ needs.
Customers are now more vocal than ever before about their experiences with brands, products, and services. There are also numerous websites, forums, and social media platforms that they can use to express their thoughts. It makes sentiment analysis an indispensable tool for monitoring customer feedback. You can automate the analysis of textual data such as reviews, social media conversations, and more. It can help you understand what aspects of your product please the customers and what frustrates them. You can use these insights to tailor products and services that live up to the customers’ expectations.
For example, sentiment analysis can help you analyze 10,000+ reviews related to your product. You can use the insights to determine if the customers are happy with your product and customer service.
You can also use it to analyze brand sentiment on social media platforms in real-time with ease. You can also identify unhappy customers and decrease your initial response time to retain them.
The prospects and advantages of sentiment analysis are limitless. Let’s dive deeper to know more about sentiment analysis.
Types of Sentiment Analysis
The sentiment analysis models focus on analyzing the sentiments expressed in any text. It dissects the emotions expressed by the authors and classifies the data into positive, negative, and neutral categories.
Businesses and brands often use it to interpret customer feedback from multiple channels. There are various ways to develop sentiment analysis models that meet your requirements. Here are some of the most popular techniques of performing sentiment analysis:
Fine-grained Sentiment Analysis
More polarity classifications, including positive and negative, can help you deal with complex polarity conditions in your organization.
- Very positive
- Very negative
This method of increasing polarity precision is known as fine-grained sentiment analysis. It is similar to the five-start rating reviews you usually find on hotels.
- Very Positive = 5 stars
- Very Negative = 1 star
This method of sentiment analysis focuses on detecting emotions. It identifies emotions such as happiness, frustration, anger, sadness, and more while analyzing text. It often uses lexicons (lists of words that carry emotions) or machine learning algorithms to examine data.
Although, the biggest downside of using this method is that people express emotions in many different ways. Words that indicate negative thoughts can also be used to express positive feedback.
Negative: Your customer support is soo bad, I regret buying your product.
Positive: This is bad-ass, I regret not trying it earlier.
Aspect-based Sentiment Analysis
When analyzing the sentiments in customer feedback, businesses want to know aspects of their product are often discussed and in what way. It can help them focus on improving the negative aspects and identify positive ones for marketing purposes. This is where aspect-based sentiment analysis can assist you. It can know what customers talk about the features of your product. For example: “The battery backup of this phone is not good”. Another example would be “The mountain hill hotel was good, but the food was really bad”.
Multilingual Sentiment Analysis
Multilingual sentiment analysis is one of the most complex methods of sentiment analysis. It usually involves pre-processing a to of data to build the model. Most of the resources are available online (lexicons), but you will have to create some (translated corpora and noise detection algorithms). Furthermore, you must also have coding experience to build the algorithm.
But if you want to avoid the hassle of building a sentiment analysis model from scratch, you can use BytesView instead. It has a language classifier, you can train custom sentiment analysis models with data related to your organization. You can then automate the classification of text in the language you want.
What makes Sentiment Analysis Important?
Today, customers use various online channels to share their experiences with various brands and products. But the sheer volume of that data makes it difficult to analyze. According to estimates, 90% of the data on the internet is unstructured. Sentiment analysis can help you automate the process of analyzing unstructured data from multiple sources.
You can dissect valuable insights from large volumes of data created every day: emails, customer support tickets, customer feedback, social media posts, blogs, news articles, documents, and more. You can also use sentiment analysis to analyze the opinions of customers about your brand and products.
Benefits of sentiment analysis tools:
- Sort Large Volumes of Data: Manually sorting massive volumes of data such as social media conversations, reviews, and surveys can be too time-consuming and inefficient. There is too much data to sort through and you will lose valuable time if you do it manually. With sentiment analysis, you can automate the process of analyzing large volumes of data efficiently and cost-effectively.
- Analyze Data in Real-time: Sentiment analysis can help you analyze user opinions in real-time. You can automate the classification of customer support tickets based on issues or queries. You can also analyze social media conversations related to your brand and campaigns. You can quickly identify key issues and take action in real-time to resolve them.
- Get a Consistent Criteria: People are frequently unable to assess the meaning of each piece of text with consistency. Humans have a success rate of 60-65 percent in determining the meaning of a text. Tagging text with sentiment is highly unreliable because it is subjective and influenced by personal experiences, thoughts, and beliefs. A centralized sentiment analysis system can benefit businesses by using the same data and criteria across all company-wide information, improving accuracy and analysis outcomes.
Understanding How Sentiment Analysis Works
Sentiment analysis, also referred to as opinion mining, uses natural language processing to interpret human language and machine learning to identify the emotions expressed in textual data.
However, there are different algorithms that users can use to build a sentiment analysis model. It all depends on the volume of data you need to analyze and how accurate you want your model to be. Here are some of the commonly used sentiment analysis models:
- Rule-based: These models perform sentiment analysis based on a pre-determined set of rules.
- Automatic: These models leverage machine learning techniques to learn from data and increase accuracy.
- Hybrid: The models combine both rule-based and automatic sentiment analysis approaches.
The rule-based approaches use a set of pre-determined rules to identify contradiction, subjectivity, or the subject from the text. The rules usually include the numerous NLP techniques related to computational linguistics. The most commonly used techniques are:
- Stemming, part-of-speech tagging (PoS), tokenization, parsing.
- Lexicons (list of words such as emotions, expressions, etc)
Here’s an example to give you an idea of how the system works:
- First, define lists of positive (bad, worst, poor, inferior) and negative words (good, great, best, excellent).
- Next, identify and count the number of positive and negative words from the text.
- If the number of positive words is more than the text has a positive sentiment. Similarly, if the number of negative words is more than the text carries negative sentiments.
Although, the limitation that rule-based systems have is that they only consider the words but not the sequence in which it is arranged. You can use more advanced processing methods and add more rules for more accurate analysis. But adding more rules may drastically alter the past results and make the analysis model more complex. These systems also need regular fine-tuning and expansion of vocabulary along with regular investments to do so.
Rather than a set of rules, automatic approaches use machine learning techniques to identify sentiments from textual data. A classifier, which accepts text as input and outputs one of several possible categories, such as positive, negative, or neutral, is frequently used to perform sentiment analysis.
Here’s how you can implement a machine learning classifier:
Automatic Approach Training and Detection Method
In the training phase, the model pairs an input (e.g., a text) with its matching output (a tag) by learning to correlate them based on the training samples. Then the feature extractor transforms the text input into vectors. Next, it uses pairs of feature vectors and tags to train the machine learning algorithm for analyzing sentiments.
The feature extractor is used in the prediction process to convert their unknown input to features. After running these feature vectors through the model, the machine will predict which tags are associated with it (positive, negative, or neutral).
Text Feature Extraction
The primary step in building a machine learning text classifier is to transform textual data into vectors. The most common method to do it is bag-of-words or bag-of ngrams with their frequency. Although, a new feature extraction method known as word embeddings is gaining popularity. It makes it feasible to assign comparable representations to words with the same meaning, thus enhancing the performance of classifiers.
Text Classification Algorithms:
The text classification process includes statistical models like Linear Regression, Deep Learning, Naïve Bayes, or Support Vector Machines.
- Linear Regression: A statistical algorithm that predicts a variable (Y) based on a set of features (X).
- Deep Learning: Utilizing artificial neural networks to analyze data to resemble the human brain with a varied array of methods
- Naïve Bayes: A type of algorithm which employs Bayes’ Theorem to assign a text to a category
- Support Vector Machines: A text-point-based model that does not use probability and instead represents each instance of text using multiple dimensions. The different opinions found in that section of the chart are mapped to different regions. Documents are also labeled based on associations with previous documents and physical locations.
Hybrid systems, as the name implies, bring together elements of rule-based and automatic systems. A major benefit of these methods is that they usually give more precise results.
Challenges Faced When Building a Sentiment Analysis Model
Sentiment analysis is probably the most difficult task in natural language processing. Even humans struggle to determine the sentiments precisely. Data scientists are creating better and more precise sentiment analysis models. But there are still some challenges that have yet to be overcome. Let’s discuss some of the challenges of sentiment analysis:
Text Subjectivity and Tone
The textual data is usually of two types: subjective and objective. The subjective texts contain sentiments but the objective texts don’t. Let’s look at an example so you can have a clear understanding of both texts:
The apple is delicious.
The apple is green.
Most people would consider the first sentence carrying a positive sentiment, whereas the second sentence will be considered neutral. Although, not all words (verbs, adjectives, and nouns) carry the same weight in terms of sentiments. In the above examples, the word ‘delicious’ is subjective and expresses sentiments.
Text Polarity and Context
Everything that can be said will be said by someone, in some place, at some point in time. But what can be said will be said in some context. Analyzing sentiments without context can decrease the accuracy of the analysis. Machines cannot learn to identify the context unless they are specifically mentioned. Another issue is the change in polarity. Let’s look at some examples to make the concept more clear:
- Unquestionably all of it.
- Definitely nothing
Now consider these as responses to the question, What is it that you liked about the game? The first response will be positive and the second negative. But what if we change the question? What if the question is, What is it that you don’t like about the game? The negative question alters the sentiment that the responses carry despite it remaining the same.
It requires a great deal of pre-processing or post-processing to effectively analyze sentiments in the right context. But figuring out how to effectively filter and use data for understanding the context of sentiments is complicated.
Irony and Sarcasm in Text
Irony and sarcasm are the most difficult sentiments for machines to detect. People often communicate negative sentiments through positive words and vice-versa. Machines will produce inaccurate results if they do not know the context in which the sentiments are expressed. For example, let’s look at one such tweet:
— Lindsey Wasson (@lindseywasson) January 30, 2020
What sentiment do you think the tweet carries? The tweet uses the negative word, “victimized”, which indicates negative sentiment. But if we look at the context, the author is praising the skills of the artist. The problem here is that the machine has no textual clue to help it learn from the data.
Treating comparisons in sentiment is another major challenge that you might face. Let’s look at a few examples:
- Nothing can beat this.
- Older tools fall short of this.
- Better than nothing.
The first sentence clearly carries clues that indicate positive sentiments.
However, the second and third texts are a bit more complex and difficult to categorize. Is it positive, negative, or neutral? Here, the context in which the statements are made is vital to identify the sentiments.
Emojis in Text
There are two types of emojis:
- Western Emojis: those encoded in one or two characters.
- Eastern Emojis: Those with a longer combination of characters of a vertical nature.
These emojis play a crucial role in expressing sentiments, especially in tweets.
Character-level, and even word-level, considerations will need to be taken into account in your sentiment analysis of tweets. This is especially true when it comes to preprocessing. You might want to preprocess tweets and convert both Western and Eastern emojis to tokens. To improve sentiment analysis performance, you could whitelist (that is, always treat emojis as a feature in classification) these tokens.
You’ll need to pay special attention to character-level, as well as word-level when performing sentiment analysis on tweets. A lot of preprocessing might also be needed. For example, you might want to preprocess social media content and transform both Western and Eastern emojis into tokens and whitelist them (i.e. always take them as a feature for classification purposes) to help improve sentiment analysis performance.
A list of emojis and their Unicode characters for pre-processing.
Determining Neutral Sentiments
Other difficulties include comprehending how neutral works when attempting to increase sentiment analysis accuracy. Defining the neutral tag is critical in this classification problem. It makes a difference which category you place sentiment analysis models in (neutral, positive, or negative). The successful application of tagged data requires a strong characterization of the problem.
Here are some ways you can accurately define neutral texts:
- Objective-sounding texts do not contain any specific sentiments. You can tag such texts in the neutral category.
- If you haven’t processed your data, it may contain irrelevant text which you can mark as neutral. However, only follow these instructions if you understand the ramifications for the entire project. The added noise can lead to a drop in accuracy.
- Generally, the text of the product contains impartial comments, such as the use of the phrase “I wish the software had more plugins.” Comparing products, such as saying I wish the software were better, is hard to classify.
Human Annotator Accuracy
Performing sentiment analysis is a challenging task even for people. The average inter-annotator agreement (how well two or more humans make the same annotation decision) is low. Moreover, with classifiers being able to learn from data, sentiment analysis classifiers could become inaccurate, compared to other classifiers.
Nevertheless, even if you are occasionally incorrect, the advantages of sentiment analysis make it worth all the effort. You can use the BytesView sentiment analysis model to get accurate predictions with maximum accuracy.
The benefits are easily noticeable if you are new to sentiment analysis. You will save money and time on tiresome manual activities by automating tasks like ticket routing, brand monitoring, and VoC analysis.
Industry Applications of Sentiment Analysis
There are numerous applications of sentiment analysis and can be used in a wide range of industries from retail, hospitality, pharmaceuticals, and even finance. Here are some of the most popular ways businesses use sentiment analysis:
Brands have a lot of user-generated content on not just social media or review platforms, but across all over the internet. This is a goldmine of valuable insights, but the massive volume of the data makes it difficult to analyze. Furthermore, while the data mentions your brand, it is mostly unstructured and difficult to analyze. But if you fail to analyze this data, you won’t be able to identify what users talk about you or find critical issues. A prime example of this is the United Airlines Flight debacle from 2017. The flight was overbooked and 4 passengers including a pulmonologist were asked. But when the pulmonologist didn’t leave his seat as he had an appointment with a patient. He was forcefully removed from the flight which resulted in injuries to the passenger. A video of the incident went viral on the internet and led to massive outrage from the people. As the airlines didn’t identify and respond immediately to the problem, the incident got the attention of users all over the internet and even lead to an official investigation on the matter. All of this happened in a matter of hours. The backlash from the social media users could have been significantly reduced if United Airlines immediately identified the issues and responded accordingly.
Marking the whole artboard will give you a single file with all the contents. To have individual assets, you should mark each for batch export. Hope this helps! ^NR
— Adobe XD (@AdobeXD) March 9, 2018
Brand monitoring can provide you valuable insights by analyzing conversations mentioning your brand all over the internet. You can track and analyze news articles, social media conversations, forums, blogs, and more with ease to understand public sentiment. You can also analyze data specific to certain demographics and get insights.
You can analyze how your brand’s image evolves and compare it with your competitors to analyze the impact of your marketing strategies. You can analyze from specific duration such as product releases, social campaigns, and more and compare them to previous performance statistics or with your competitors. Furthermore, real-time analysis of data can help you identify PR mistakes that can become a social media crisis.
One of the best examples of this is the social media management by JCPenney. Many social media users claimed that the JCPenney teapot resembled Hitler and the conversation started gaining traction. But before it could. explode and go viral, JCPenney identified the conversation and made it clear that it was a coincidence and was in no way intentional.
— The Telegraph (@Telegraph) May 28, 2013
@jcpenney responded to numerous tweets related to the evil teapot with a light-hearted message to clear the misunderstanding. Also, the fiasco resulted in a good thing as they saw huge numbers in teapot sales.
Social Media Monitoring
Consumers are now much more vocal about their experiences. They often use social media channels to do express their opinion, positive or negative. Sentiment analysis tools allow you to analyze these conversations with ease and understand how customers feel.
Thanks for reaching out, Fred. We’re so sorry for the trouble with these charges. Please shoot us a DM with your email on file and we’d be happy to look into this further for you.
— ClassPass (@classpass) August 30, 2021
You can identify key issues that customers face and detect issues that may spiral out of control if not acted upon immediately. You can also check what customers think about a particular product or a newly added feature.
One of the best examples of this would be Domino’s Easter Sunday Youtube video. Two employees, counting on their popularity made and shared a Youtube video. In it, they showed how Domino’s prepares food in its restaurants.
However, the process was shown in a remarkably disgusting way. While we are not going to get into the details, but watching the video will surely end your appetite. The video gained massive traction on the internet, but for the wrong reasons. The video has over a million views, and Domino’s ranked higher in search results although through negative sentiments of the customers.
However, the damage could have been minimized if Domino’s had acted at the right time. If the marketing team had tracked all related conversations and analyzed sentiments, they could have removed the video and stopped the situation from going out of control.
Social media chatter grows at an exponential rate. Thousands, if not millions of users express their opinions, debate, and argue at the same time, and all of this happens in minutes. If you do not track critical issues and conversations, your brand could go viral but for the wrong reasons, thus damaging your reputation.
Sentiment analysis can process large volumes of data that are invaluable for competitive research and market analysis. Sentiment analysis is critical when entering a new market, forecasting trends, or determining how to best compete with your competitors.
You can examine customer feedback on your products to see how you compare to your competitors in the market. Your competitor may have launched a new product that proved to be a flop. Find out which features of the product were the least successful and use that information to your advantage.
Keep an eye on your brand’s and competitors’ social media accounts to see how they’re doing. Locate areas where your brand is likely to succeed. Learn what’s popular as soon as it happens, or research formal market reports and business publications to get a handle on long-term trends.
You can measure and count previously unquantifiable information by discovering and counting new information sources. Because public data is frequently limited in emerging markets, social data analysis can fill in the gaps.
We discussed how we can apply sentiment analysis across the organization, so we’ll now narrow in on customer service.
Excellent customer service leads to increased repeat purchases. A successful business should know that it is just as crucial to delivering on time as it is to offer the right product. People want every business interaction to be about feelings, emotions, and experiences, instead of focusing on money or details. If they don’t stay, they’ll depart and take their business somewhere. Do you realize that for every single negative experience with a brand, about one-third of customers will desert it?
Look over your employees’ interactions with customers to make sure they are following the correct protocol. Make the process more efficient so customers are don’t have to wait for support. Increase your initial response time and provide an excellent experience so customers stay loyal to your brand.
Also, ook over your employees’ interactions with customers to make sure they are following the correct protocol.
Voice of Customer (VOC)
Social media conversations and brand monitoring offer real-time insights about your brand. However, you can also use reviews, surveys, and customer support interactions to get insights. Net Promoter Score (NPS) is one of the most common methods to gain feedback from customers. They usually include questions like: Would you personally tell a family member or friend about this company, product, and/or service? This yields a single score that uses numbers. Businesses use the scores to categorize customers into promoters, detractors, and passive. It helps them measure the overall customer experience and identify ways to enhance it so the customers stick to your brand.
Aggregating and assessing quantified data is simpler than more qualitative data. Next, NPS asks participants why they gave the score they did, and the results are entirely qualitative.
It was impossible to assess the open-ended survey replies in the past, but with sentiment analysis the texts may be placed into positive and negative categories, providing further insights into the Voice of the Customer (VoC).
Any form of a survey, whether it is quantitative or qualitative, or even responses to customer support conversations, can utilize sentiment analysis to uncover your consumers’ feelings and ideas. Tracking customer sentiment over time allows you to get an understanding of why your customer sentiment has changed, including understanding why their NPS scores have shifted or how they feel about certain areas of your organization.
You could utilize it to spot consumers who are “very negative” in regards to service, especially on feedback or support tickets. For better results, you should focus on key demographics.
And you can track mentions of your brand in real-time and make subtle observations to pinpoint fine details without depending on percentages and stats.
The Best Sentiment Analysis Tools in 2021
It’s challenging to know where to start with such a broad field as sentiment analysis. Fortunately, you can start by using several types of free tools and tutorials from the internet.
Free Sentiment Analysis Tools
To learn how a sentiment analysis tool works, you can start by testing some free sentiment analysis tools. You can experience the features, benefits, and understand how it works.
BytesView has several pre-trained models for various sentiment analysis tasks. Click on the following tab to see the various sentiment analysis models.
If you get an unexpected result, it’s possible that the model didn’t understand certain words or phrases (yet). Insert more data to see how the results change.
You can also tailor sentiment analysis models to the needs of your company or organization by populating your models with company-specific data.
Sentiment Analysis Use Cases
Now that you know what sentiment analysis is, let’s discuss the ways you can use it and extract insights.
General Sentiment Analysis
If you are not sure about where to get started, the general sentiment analysis model is the perfect way to get started It is a sentiment classifier for English, and you can use it to analyze various random sentences.
Analyzing product reviews can give you access to various insights. You can identify the aspects that have positive, negative, and neutral sentiments. Based on the findings, you can focus on the negative aspects of the product and optimize it for a better customer experience.
Hospitality is an industry where customer reviews highly influence the decision-making of consumers. Good customer reviews can attract more customers whereas negative reviews can make you lose customers.
Twitter Data Analysis
This model examines the sentiments expressed in Twitter posts. It’s an excellent tool for staying up to date on social listening and monitoring consumer sentiment in real-time.
Open Source VS Saas-Based Tools
Concerning sentiment analysis or text analysis as a whole, you have two options: design your solution or purchase a tool.
Within these communities, data science in the form of natural language processing and deep learning is popular. Using open-source libraries for languages such as Python and Java, you could easily build your sentiment analysis solution. However, you’ll need a data science and engineering team on board, as well as significant investments and time.
Sentiment analysis with SaaS tools typically takes only a few minutes and a few simple steps. You can get started quickly by hiring or assembling a data science or engineering team, or you can skip coding entirely and implement AI with no or limited coding.
The fact that you don’t need to know how to code to use SaaS solutions like the BytesView Zendesk, Excel, and Zapier Integrations is a huge plus (for example).
To use any of these cutting-edge solutions, you should first read our guide to the best SaaS tools for sentiment analysis, which highlights all of the available APIs for easily integrating your existing software into these solutions.
Alternatively, you can begin learning how to do sentiment analysis with just six lines of code by utilizing BytesView’s API and pre-built sentiment analysis models. You can also train your own unique sentiment analysis models with your industry-specific data.
Open Source Sentiment Analysis Tools
If you still want to build your custom sentiment analysis solution, here are some of the widely used open-source tools:
Scikit-learn: Use the popular Scikit-learn toolkit and its useful text vectorization features to work on machine learning. Using vectorizers to build a classifier, such as frequency or tf-idf text vectorizers, is a simple process. Many learning algorithms are supported by Scikit-learn, including support vector machines, naive Bayes, and logistic regression.
NLTK: Python programmers frequently rely on NLTK, their preferred NLP library. It has a thriving community and a classifier training option.
SpaCy: SpaCy is a library with an NLP enthusiast community. Its toolkit, like NLTK’s, includes powerful NLP and text classifier tools.
TensorFlow: TensorFlow, a Google platform, provides a set of basic tools for building and training neural networks. It provides text vectorization in addition to basic word frequency and more sophisticated cross-word embeddings.
Keras: Keras provides useful abstractions for working with recurrent neural networks (RNNs), convolutional neural networks (CNNs), and other types of neural networks, making neuron layers stackable. Keras can be used as a foundation for Tensorflow or Theano. It provides more than just classification tools. They are also extremely useful.
PyTorch: Backed by Facebook, Twitter, Nvidia, Salesforce, Stanford University, the University of Oxford, and Uber, PyTorch is one of the most recent machine-learning frameworks. Because of its rapid development, it now has a strong community.
The NLP library available in Java is not the only example of an impressive data science library that is supported by a robust community of Java coders.
Open NLP: A framework, supported by a large library of models and algorithms, that assists with a variety of important tasks, including tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, and parsing.
Stanford Core NLP: The Stanford NLP Group developed Stanford CoreNLP, a Java toolset containing core NLP programs.
Lingpipe: A Java toolkit used for computationally processing text. Text classification and entity extraction both frequently employ LingPipe.
Weka: The University of Waikato’s Weka, a software suite including capabilities for data processing, classification, regression, clustering, pattern recognition, and data visualization.
Python Web Scraping and Sentiment: This tutorial, which provides an overview of Python web scraping and sentiment analysis, includes a step-by-step explanation of how to analyze the top 100 subreddits by sentiment. Beautiful Soup, one of the most popular Python packages for web scraping, explains how to use it. It compiles the titles of popular subreddit pages such as /r/funny, /r/AskReddit, and /r/todayilearned.
It demonstrates how to use the Reddit API, interact with these subreddits, and extract their comments. Then, use TextBlob to analyze the sentiment of the retrieved comments. Code: The complete code can be found on GitHub at https://github.com/jg-fisher/reddit. Sentiment
Twitter sentiment analysis with Python and NLTK: To begin, go through this detailed tutorial for training your first sentiment classifier. The author uses NLTK’s Natural Language Toolkit on tweets to train a classifier. To make it simple, learn how to do sentiment analysis with
Scikit-learn: This guide explains how to train a sentiment analysis logistic regression model. This guide explains how to train a sentiment analysis logistic regression model.
Sentiment Analysis Research and Studies
Now that you have a basic understanding of sentiment analysis, along with the various options available in the industry, you should dive further into the topic. Here are some information sources that you can check out.
Sentiment Analysis Datasets
The key to building an effective sentiment analysis solution is, analyzing various datasets and testing the different approaches. You need to accumulate a substantial volume of data to perform your research and testing.
Here are some datasets that you can use for experimenting with sentiment analysis. They are available for free download on the internet.
- Restaurant reviews: 5.2 million Yelp reviews along with star ratings.
- Fine-dining reviews: Amazon’s dataset has roughly 500,000 meal reviews. And each review has a plain text version as well as product and user information.
- Product reviews: The dataset contains millions of Amazon customer reviews with star ratings, which is perfect for training sentiment analysis models.
- Movie rating tweets: This dataset comprises 1,000 positive and 1,000 negative reviews. It also includes 5,331 positive and negative processed remarks and phrases.
- Apple INC: This data set includes tweets about Apple Inc. It was gathered to examine user reactions about Apple INC.
- Stock market-related tweets: This collection is made up of tweets sharing financial news. Of the Twitter messages that were studied, 3,685 were positive, and 2,106 were negative.
- Although, if you are experimenting with rule-based sentiment analysis techniques, lists of lexicons can help you out. Here are a collection of lexicons (lists of words with labels indicating the sentiment they carry) that you can use to fuel your research and testing.
- Sentiment Lexicons for 81 Languages: In this dataset, there are lexicons including both positive and negative sentiments in 81 languages.
- SentiWordNet: With around 29,000 words, it contains sentiment scores ranging from 0 to 1.
- Wordstat Sentiment Dictionary: Around 5000 positive and 9000 negative terms are found in this sample.
- Opinion Lexicon for Sentiment Analysis: The dataset contains 4,782 English terms that are considered negative, and 2,005 words that are seen as positive.
- Emoticon Sentiment Lexicon: A list of 477 emoticons, categorized as either positive, neutral, or negative, is in this dataset.
Sentiment Analysis Papers
There are hundreds of thousands of academic papers, reports, and books about sentiment analysis.
Many academics in the sentiment analysis domain point to the following articles as leading the field in various ways:
- Opinion mining and sentiment analysis (Pang and Lee, 2008)
- Emotion Recognition in Conversations
- Recognizing contextual polarity in phrase-level sentiment analysis (Wilson, Wiebe, and Hoffmann, 2005).
- Bilingual Emotion Lexicon
- A survey of opinion mining and sentiment analysis (Liu and Zhang, 2012)
- Multilingual Twitter Sentiment Classification
- Sentiment analysis and opinion mining (Liu, 2012)
- How to Perform Text Mining with Sentiment Analysis
- Systematic Reviews in Sentiment Analysis
Sentiment Analysis Courses
Studying Natural Language Processing (NLP), the computer science domain that focuses on human language interpretation is another successful method of deep sentiment analysis. NLP enables machines to better understand the sentiment, assessments, attitudes, and emotions found in written language, which has a wide range of applications in everyday interactions.
Stanford Coursera course by Dan Jurafsky and Christopher Manning is the fundamental NLP course. There are numerous resources and lectures available on the internet, but the Stanford Coursera course is the primary course required to learn NLP. This course introduces you to the subject through two of the most well-known NLP figures, who will guide you through an in-depth process.
If you prefer more interactive learning, check out the Data Science: Natural Language Processing (NLP) in Python course by Udemy. This course will provide you with a thorough introduction to NLP and how it can be used. This will entail completing various Python-based projects such as a spam detector, sentiment analyzer, and article spinner. The course consists of approximately 5-minute lectures that are academic without being overwhelming.
Sentiment Analysis Books
Bing Liu is a leading machine learning expert who specializes in sentiment analysis and opinion mining, topics covered in his book.
Liu does an excellent job of explaining the complexities of sentiment analysis while keeping it simple for beginners. One example is his work on Sentiment Analysis, a popular machine learning method that can be used to compute student and teacher confidence levels as well as paraphrase recommendations.
The Deep-Learning-Based Technologies for Sentiment Analysis book is intended for people who want to investigate sentiments using machine learning and AI approaches.
You can refer to books such as:
Many corporate operations, such as brand monitoring, product analytics, customer service, and market research, could benefit from sentiment analysis. Leading brands are looking for ways to work faster and more accurately to achieve greater efficiency and productivity.
Sentiment analysis has progressed from a cool technological fad to a critical requirement for all businesses. Sentiment analysis helps us learn more about our customers, understand our employees, and better serve both over time.
BytesView is an online platform that enables you to process large volumes of data and extract insights with ease. It also allows you to build custom solutions for your organization. Click here to request a demo.