October 14, 2021Uncategorized
Sentiment analysis is the application of text mining tools to identify the objective behind a piece of specified information and predicting the positive or negative outcomes out of it. Sentiment analysis helps businesses primarily to analyze social sentiments and get informed about the voice of the customers regarding a brand, product, or service. Sentiment analysis today has become one of the most reliable and applicable technologies to capture and process the sentiments of every person related to sustainable business practices and growth.
With an increasing amount of businesses driven towards digital transformation practices, it is important to understand their social brand presence. And to understand the voice of the audience for any brand, social media sentiment analysis is the go-to-market strategy for effective and result-driven market outcomes. In this article, we will explore the concept of sentiment analysis and how to conduct social media sentiment analysis for Twitter.
Social Sentiment Analysis – Conceptualization
Social media sentiment analysis is one of the most prominent techniques to analyze your customer’s needs and modernize your services for efficient outcomes. Social media sentiment analysis is defined as a practice that computationally identifies and categorizes various attributes in a piece of data to discover whether the writer’s attitude towards a post or comment is positive, negative, or neutral. Social sentiment analysis in the age of digitization helps brands and businesses to not only understand the market outcomes for their products/services but also provides an opportunity to forecast and rectify the current operations. To better understand how a social sentiment analysis will work, let us have a wider view about performing sentiment analysis on any data obtained.
Sentiment Analysis – How is it done?
Every data analyzing process has a definitive procedure to determine the facts and maintain the accuracy of the model. Below we have defined how a basic sentiment analysis model works:
Step 1: Tokenization.
Tokenization involves dividing the given statement into a different set of words or dividing the given paragraph into a different set of statements.
For instance, the given statement is -” This flavor of chocolate tastes bitter”
The given statement is further divided into a different set of words, like this, flavor, of, chocolate, tastes, bitter.
Step 2: Cleaning the data.
Once the step of tokenization is done, we perform the data cleaning. In this stage, we remove special characters in the data that add no value to analytics. As the above-given statement doesn’t have any special characters we skip this step.
Step 3: Remove the stop words.
Similar to step 2, during the third step we remove all the words that don’t add any value to the analytics. For instance, the words like “this and of” are removed for a better understanding of the sentiment of the above statement.
Step 4: Classification and Calculation
During the classification process, we define the remaining words in the statement as either positive, negative, or neutral. Every positive word gets a score of ‘+1’ while a negative word is scored with a ‘-1’, and a neutral word is given ‘0’ as a score. During this stage, we apply supervised machine learning algorithms to identify the given word as positive, negative, or neutral. During classification, you can train your ML model to identify defined words with particular sentiments or you can add lexicons (a dictionary of pre-classified words) to perform sentiment analysis.
From the above statement, the left-out words are – flavor, chocolate, tastes, bitter.
We score flavor (o) as it is a neutral word, chocolate (0) again a neutral word, tastes (0) again a neutral, and bitter (-1) as it is a negative sentiment. Once we have done scoring, we calculate the final sentiment score. In this case, the score is 0+0+0-1 = -1, since the polarity is lesser than zero the given statement is ‘negative’.
Different levels of sentiment classification
Once we find out the sentiment of a given statement, to define the polarity of a sentence i.e., whether the statement expresses sentiment classification. If yes then whether it is a binary classification (positive or negative) or multi-class classification (extremely negative or extremely neutral, or neutral). This kind of analysis can be done at several levels of granularity.
Word Level: word-level sentiment analysis is the breakdown of a sentence and document into words that are strongly adjectives to define the polarity of words for further defining the sentiment of the sentence.
Sentence Level: sentence level or phrase-level analysis deals with tagging individual sentences into their respective sentiment polarities through determining the sentiment of each word and combining them to determine the sentiment of the whole phrase.
Document Level: it deals with tagging individual documents either into positive or negative sentiment. The sentiment classification works similarly, first, we identify the sentiment of each word and then combine them to identify the sentiment of each sentence and combine all sentence scores to identify paragraphs, and so on to define the sentiment of a document.
Feature Level: Aspect level or feature level sentiment classification deals with labeling each word with their sentiment and also determining the entity towards which sentiment is directed.
Sentiment Analysis on Social Media – Why is it Important?
Social media has become the main channel for communication for the majority of businesses across the globe. Irrespective of the social medium you use to promote, interact, and entertain your customers, today’s social channels are filled with unfiltered insights for every brand ever existed. Opinion mining popularly also known as sentiment analysis can be an effective technique to understand, modernize, and scale your user operations for exceptional business outcomes.
Leveraging NLP techniques and applying smart machine learning supervised algorithms helps understand the polarity measures and subjectivity measures of any given text for better understanding the texture and context of any given sentiment. Polarity measure defines how positive or how negative a statement is, where subjectivity is more about defining the personal feelings, the context of the statement, views, and beliefs of a person defining a particular statement. For better understanding a subjective measure of sentiment, we categorize the given text as a subjective sentence and subjective expression. While the subjective sentence is a neutral opinion that doesn’t affect the sentiment results, subjective expression clearly defines strong opinions like beliefs, allegations, suspensions, etc. Performing in-depth sentiment analysis can help brands in determining the opinions and beliefs about a product/service and provides a clear vision on how to scale up the expected results for the next business years.
Sentiment analysis for analyzing social audience opinions has a wider impact than we ever imagined. With the increasing availability of data and more transparent communication channels, businesses can harness the power of social media efficiently than ever before. Social sentiment analysis is changing the way businesses advertise and market products to their target groups for effective reach and definitive conversions. Sentiment analysis will continue to grow as businesses work towards digitization. And working on this technique has become a priority for most businesses to increase their customer retention ratio. To better understand how to leverage sentiment analysis for your business or to integrate pre-built sentiment analysis rest API, contact Deeplobe.
Sentiment analysis is the application of text mining tools to identify the objective behind a piece of specified information and predicting the positive or negative outcomes out of it. Sentiment analysis helps businesses primarily to analyze social sentiments and get informed about the voice of the customers regarding a brand, product, or service. Sentiment analysis today has become one of the most reliable and applicable technologies to capture and process the sentiments of every person related to sustainable business practices and growth.
With an increasing amount of businesses driven towards digital transformation practices, it is important to understand their social brand presence. And to understand the voice of the audience for any brand, social media sentiment analysis is the go-to-market strategy for effective and result-driven market outcomes. In this article, we will explore the concept of sentiment analysis and how to conduct social media sentiment analysis for Twitter.
Social Sentiment Analysis – Conceptualization
Social media sentiment analysis is one of the most prominent techniques to analyze your customer’s needs and modernize your services for efficient outcomes. Social media sentiment analysis is defined as a practice that computationally identifies and categorizes various attributes in a piece of data to discover whether the writer’s attitude towards a post or comment is positive, negative, or neutral. Social sentiment analysis in the age of digitization helps brands and businesses to not only understand the market outcomes for their products/services but also provides an opportunity to forecast and rectify the current operations. To better understand how a social sentiment analysis will work, let us have a wider view about performing sentiment analysis on any data obtained.
Sentiment Analysis – How is it done?
Every data analyzing process has a definitive procedure to determine the facts and maintain the accuracy of the model. Below we have defined how a basic sentiment analysis model works:
Step 1: Tokenization.
Tokenization involves dividing the given statement into a different set of words or dividing the given paragraph into a different set of statements.
For instance, the given statement is -” This flavor of chocolate tastes bitter”
The given statement is further divided into a different set of words, like this, flavor, of, chocolate, tastes, bitter.
Step 2: Cleaning the data.
Once the step of tokenization is done, we perform the data cleaning. In this stage, we remove special characters in the data that add no value to analytics. As the above-given statement doesn’t have any special characters we skip this step.
Step 3: Remove the stop words.
Similar to step 2, during the third step we remove all the words that don’t add any value to the analytics. For instance, the words like “this and of” are removed for a better understanding of the sentiment of the above statement.
Step 4: Classification and Calculation
During the classification process, we define the remaining words in the statement as either positive, negative, or neutral. Every positive word gets a score of ‘+1’ while a negative word is scored with a ‘-1’, and a neutral word is given ‘0’ as a score. During this stage, we apply supervised machine learning algorithms to identify the given word as positive, negative, or neutral. During classification, you can train your ML model to identify defined words with particular sentiments or you can add lexicons (a dictionary of pre-classified words) to perform sentiment analysis.
From the above statement, the left-out words are – flavor, chocolate, tastes, bitter.
We score flavor (o) as it is a neutral word, chocolate (0) again a neutral word, tastes (0) again a neutral, and bitter (-1) as it is a negative sentiment. Once we have done scoring, we calculate the final sentiment score. In this case, the score is 0+0+0-1 = -1, since the polarity is lesser than zero the given statement is ‘negative’.
Different levels of sentiment classification
Once we find out the sentiment of a given statement, to define the polarity of a sentence i.e., whether the statement expresses sentiment classification. If yes then whether it is a binary classification (positive or negative) or multi-class classification (extremely negative or extremely neutral, or neutral). This kind of analysis can be done at several levels of granularity.Word Level: word-level sentiment analysis is the breakdown of a sentence and document into words that are strongly adjectives to define the polarity of words for further defining the sentiment of the sentence.
Sentence Level: sentence level or phrase-level analysis deals with tagging individual sentences into their respective sentiment polarities through determining the sentiment of each word and combining them to determine the sentiment of the whole phrase.
Document Level: it deals with tagging individual documents either into positive or negative sentiment. The sentiment classification works similarly, first, we identify the sentiment of each word and then combine them to identify the sentiment of each sentence and combine all sentence scores to identify paragraphs, and so on to define the sentiment of a document.
Feature Level: Aspect level or feature level sentiment classification deals with labeling each word with their sentiment and also determining the entity towards which sentiment is directed.
Sentiment Analysis on Social Media – Why is it Important?
Social media has become the main channel for communication for the majority of businesses across the globe. Irrespective of the social medium you use to promote, interact, and entertain your customers, today’s social channels are filled with unfiltered insights for every brand ever existed. Opinion mining popularly also known as sentiment analysis can be an effective technique to understand, modernize, and scale your user operations for exceptional business outcomes.
Leveraging NLP techniques and applying smart machine learning supervised algorithms helps understand the polarity measures and subjectivity measures of any given text for better understanding the texture and context of any given sentiment. Polarity measure defines how positive or how negative a statement is, where subjectivity is more about defining the personal feelings, the context of the statement, views, and beliefs of a person defining a particular statement. For better understanding a subjective measure of sentiment, we categorize the given text as a subjective sentence and subjective expression. While the subjective sentence is a neutral opinion that doesn’t affect the sentiment results, subjective expression clearly defines strong opinions like beliefs, allegations, suspensions, etc. Performing in-depth sentiment analysis can help brands in determining the opinions and beliefs about a product/service and provides a clear vision on how to scale up the expected results for the next business years.
Sentiment analysis for analyzing social audience opinions has a wider impact than we ever imagined. With the increasing availability of data and more transparent communication channels, businesses can harness the power of social media efficiently than ever before. Social sentiment analysis is changing the way businesses advertise and market products to their target groups for effective reach and definitive conversions. Sentiment analysis will continue to grow as businesses work towards digitization. And working on this technique has become a priority for most businesses to increase their customer retention ratio. To better understand how to leverage sentiment analysis for your business or to integrate pre-built sentiment analysis rest API, contact Deeplobe.
