Sentiment Analysis in Data Science

Sentiment Analysis in Data Science

Sentiment analysis is a concept of mining information and emotion from the text from different sources. Organizations take the help of sentiment analysis for taking the customer/user review and social sentiments about their brand, product or service while monitoring online conversations. On Social Media, we generally count the views and number of interactive text without getting a deeper understanding of high-value insight that is actually a point of concern.

Behavioral economics and psychology show us that much of human decision-making is based in the world of emotion and cognitive bias, not logic," Peter observes.

Companies crave to make use of that insightful information, so to determine the feeling of the user behind that expression. Sentiment Analysis of data science is designed and builds in such a way where the variety of elements can be provided to customers for experiences and form an opinion. Unlike many other aspects of Data Science here, we regulate the reason, due to which we get a positive or negative response from the user. It is an explanation of what organizations are likely to do as a result of feeling that way.

Sentiment analysis from text is further divided into objective and Subjective text, which depicts different meanings and concept altogether. Effective than objective text, Subjective text that is generally uttered by a human having typical moods, emotions, and feelings. Typically seen on social media, sentiments analysis is widely used to detect the feeling behind every action and reaction, be it a business, a recent movie, or a product launch, to understand its reception by the people and what they think of it based on their opinions or, you guessed it, sentiment!

Further, several levels of sentiment analysis for text can be computed into an individual sentence level, paragraph level, or the entire document as a whole. Mawkishness is computed on the document as a whole or some aggregations are done after computing the sentiment for individual sentences. There are two major approaches to sentiment analysis.

·         Supervised machine learning or deep learning approaches

·         Unsupervised lexicon-based approaches

Various popular lexicons are used for the sentiment analysis, including the following.

·         AFINN lexicon

·         Bing Liu’s lexicon

·         MPQA subjectivity lexicon

·         SentiWordNet

·         VADER lexicon

·         TextBlob lexicon

Data Science Course offers such aspects to be analyzed to find valuable means out of the analysis. Information can be found in any form in a collective form it’s called Data Science but the techniques used in analyzing a variety of data differs depending upon the nature of data. Here we discussed the overview of what are the focus areas for sentiment analysis and how the companies, organizations, ventures of any genre detects the sentiment and feedback of their offering. In Data Science Course one got to learn how to create endless opportunities and make wise use of it going forward.

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