{"id":300,"date":"2021-09-01T10:28:00","date_gmt":"2021-09-01T10:28:00","guid":{"rendered":"https:\/\/www.bytesview.com\/blog\/?p=300"},"modified":"2023-08-29T07:00:19","modified_gmt":"2023-08-29T07:00:19","slug":"sentiment-analysis","status":"publish","type":"post","link":"https:\/\/www.bytesview.com\/blog\/sentiment-analysis\/","title":{"rendered":"Sentiment Analysis: Everything You Need to Know"},"content":{"rendered":"[vc_row type=”in_container” full_screen_row_position=”middle” column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” scene_position=”center” text_color=”dark” text_align=”left” row_border_radius=”none” row_border_radius_applies=”bg” overflow=”visible” overlay_strength=”0.3″ gradient_direction=”left_to_right” shape_divider_position=”bottom” bg_image_animation=”none”][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” bottom_margin=”220″ column_element_direction_desktop=”default” column_element_spacing=”default” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” sticky_content=”true” sticky_content_functionality=”js” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” column_position=”default” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1\/4″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid” column_padding_type=”default” gradient_type=”default” offset=”vc_hidden-sm vc_hidden-xs”][\/vc_column][vc_column right_padding_desktop=”40″ right_padding_tablet=”0″ right_padding_phone=”0″ top_margin=”-230″ top_margin_tablet=”-180″ top_margin_phone=”-135″ column_element_direction_desktop=”default” column_element_spacing=”default” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” column_position=”default” el_class=”text_block_wrapper” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”3\/4″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid” column_padding_type=”advanced” gradient_type=”default” offset=”vc_col-lg-9 vc_col-md-12″][image_with_animation image_url=”339″ image_size=”full” animation_type=”entrance” animation=”None” animation_movement_type=”transform_y” hover_animation=”none” alignment=”” border_radius=”none” box_shadow=”none” image_loading=”default” max_width=”100%” max_width_mobile=”default” margin_bottom=”20″][vc_column_text]\n
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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.<\/span><\/p>\n<\/div>\n

\n

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.<\/span><\/span><\/p>\n<\/div>\n[\/vc_column_text][vc_video link=”https:\/\/www.youtube.com\/watch?v=62Q_Rd_UnnI” align=”center”][vc_custom_heading text=”What is Sentiment Analysis?” use_theme_fonts=”yes”][vc_column_text]\"what<\/p>\n

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.<\/p>\n

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.\u00a0<\/span><\/p>\n

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.\u00a0<\/span><\/p>\n

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.\u00a0<\/span><\/p>\n

The prospects and advantages of sentiment analysis are limitless. Let’s dive deeper to know more about sentiment analysis.\u00a0<\/span><\/p>\n

Types of Sentiment Analysis<\/span><\/strong><\/h2>\n

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.\u00a0<\/span><\/p>\n

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:<\/span><\/p>\n

Fine-grained Sentiment Analysis\u00a0<\/strong><\/h3>\n

\"how<\/span><\/p>\n

More polarity classifications, including positive and negative, can help you deal with complex polarity conditions in your organization.<\/span><\/p>\n

    \n
  • Very positive<\/span><\/li>\n
  • Positive<\/span><\/li>\n
  • Neutral<\/span><\/li>\n
  • Negative<\/span><\/li>\n
  • Very negative<\/span><\/li>\n<\/ul>\n

    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.\u00a0<\/span><\/p>\n

      \n
    • Very Positive = 5 stars<\/span><\/li>\n
    • Very Negative = 1 star<\/span><\/li>\n<\/ul>\n

      Emotion Detection\u00a0<\/span><\/strong><\/h3>\n

      \"emotion<\/span><\/p>\n

      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.\u00a0<\/span><\/p>\n

      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.\u00a0<\/span><\/p>\n

      Examples:<\/span><\/strong>\u00a0<\/span><\/p>\n

      Negative:<\/span><\/strong>\u00a0Your customer support is soo\u00a0bad<\/strong>, I\u00a0regret<\/strong>\u00a0buying your product.<\/span><\/p>\n

      Positive:<\/span><\/strong>\u00a0This is\u00a0bad<\/strong>-ass, I\u00a0regret<\/strong>\u00a0not trying it earlier.<\/span><\/p>\n

      Aspect-based Sentiment Analysis<\/span><\/strong><\/h3>\n

      \"how<\/span><\/p>\n

      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”.<\/span><\/p>\n

      Multilingual Sentiment Analysis\u00a0<\/span><\/strong><\/h3>\n

      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.\u00a0<\/span><\/p>\n

      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.<\/span><\/p>\n

      What makes Sentiment Analysis Important?<\/span><\/strong><\/h2>\n

      \"why<\/span><\/p>\n

      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.\u00a0<\/span><\/p>\n

      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.\u00a0<\/span><\/p>\n

      Benefits of sentiment analysis tools:<\/span><\/strong><\/h3>\n
        \n
      • Sort Large Volumes of Data:\u00a0<\/span><\/strong>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.<\/li>\n<\/ul>\n
          \n
        • Analyze Data in Real-time:\u00a0<\/span><\/strong>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.<\/li>\n<\/ul>\n
            \n
          • Get a Consistent Criteria:\u00a0<\/span><\/strong>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.<\/span><\/li>\n<\/ul>\n[\/vc_column_text][vc_custom_heading text=”Understanding How Sentiment Analysis Works” use_theme_fonts=”yes” css=”.vc_custom_1690443485177{padding-top: 30px !important;padding-bottom: 10px !important;}”][vc_column_text]\"how<\/p>\n

            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.<\/p>\n

            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:<\/span><\/p>\n

              \n
            • Rule-based:\u00a0<\/span><\/strong>These models perform sentiment analysis based on a pre-determined set of rules.<\/span><\/li>\n
            • Automatic:\u00a0<\/span><\/strong>These models leverage machine learning techniques to learn from data and increase accuracy.<\/span><\/li>\n
            • Hybrid:\u00a0<\/span><\/strong>The models combine both rule-based and automatic sentiment analysis approaches.\u00a0<\/span><\/li>\n<\/ul>\n

              Rule-based Approach\u00a0<\/span><\/strong><\/h3>\n

              \"rule-based<\/span><\/p>\n

              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:<\/span><\/p>\n

                \n
              • Stemming, part-of-speech tagging (PoS), tokenization, parsing.<\/span><\/li>\n
              • Lexicons (list of words such as emotions, expressions, etc)<\/span><\/li>\n<\/ul>\n

                Here’s an example to give you an idea of how the system works:\u00a0<\/span><\/p>\n

                  \n
                1. First, define lists of positive (bad, worst, poor, inferior) and negative words (good, great, best, excellent).<\/span><\/li>\n
                2. Next, identify and count the number of positive and negative words from the text.\u00a0<\/span><\/li>\n
                3. 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.<\/span><\/li>\n<\/ol>\n

                  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.\u00a0<\/span><\/p>\n

                  Automatic Approach<\/span><\/strong><\/h3>\n

                  \"automatic<\/span><\/p>\n

                  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.<\/span><\/p>\n

                  Here’s how you can implement a machine learning classifier:\u00a0<\/span><\/p>\n

                  Automatic Approach Training and Detection Method<\/span><\/strong><\/h4>\n

                  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.\u00a0\u00a0<\/span><\/p>\n

                  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).<\/span><\/p>\n

                  Text Feature Extraction<\/span><\/strong>\u00a0<\/span><\/h4>\n

                  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.<\/span><\/p>\n

                  Text Classification Algorithms:<\/span><\/strong><\/h4>\n

                  The text classification process includes statistical models like Linear Regression, Deep Learning, Na\u00efve Bayes, or Support Vector Machines.<\/span><\/p>\n

                    \n
                  • Linear Regression:<\/span><\/strong>\u00a0A statistical algorithm that predicts a variable (Y) based on a set of features (X).<\/span><\/li>\n
                  • Deep Learning:<\/span><\/strong>\u00a0Utilizing artificial neural networks to analyze data to resemble the human brain with a varied array of methods<\/span><\/li>\n
                  • Na\u00efve Bayes:<\/span><\/strong>\u00a0A type of algorithm which employs Bayes’ Theorem to assign a text to a category<\/span><\/li>\n
                  • Support Vector Machines:<\/span><\/strong>\u00a0A 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.<\/span><\/li>\n<\/ul>\n

                    Hybrid Approach<\/span><\/strong><\/h3>\n

                    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.<\/span><\/p>\n

                    Challenges Faced When Building a Sentiment Analysis Model<\/span><\/strong><\/h2>\n

                    \"opinion<\/span><\/strong><\/h2>\n

                    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:<\/span><\/p>\n

                    Text Subjectivity and Tone\u00a0<\/span><\/strong><\/h3>\n

                    \"sentiment<\/span><\/p>\n

                    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:\u00a0<\/span><\/p>\n

                    The apple is delicious.<\/span><\/p>\n

                    The apple is green.<\/span><\/p>\n

                    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.\u00a0<\/span><\/p>\n

                    Text Polarity and Context\u00a0<\/span><\/strong><\/h3>\n

                    \"sentiment<\/span><\/p>\n

                    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:\u00a0<\/span><\/p>\n

                      \n
                    • Unquestionably all of it.<\/span><\/li>\n
                    • Definitely nothing\u00a0<\/span><\/li>\n<\/ul>\n

                      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.\u00a0<\/span><\/p>\n

                      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.<\/span><\/p>\n

                      Irony and Sarcasm in Text<\/span><\/strong><\/h3>\n

                      \"opinion<\/span><\/p>\n

                      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:<\/span><\/p>\n

                      \n

                      \u201cRaise your hand if you have ever felt personally victimized by @ErikaJSchultz<\/a>\u2019s photography skills\u201d pic.twitter.com\/KrgLYuMz4r<\/a><\/p>\n

                      \u2014 Lindsey Wasson (@lindseywasson) January 30, 2020<\/a><\/p><\/blockquote>\n