Sangeeta Apr 17, 2014 No Comments
How do the various political Manifestos compare with respect to important keywords?
What do twitter users have to say about the new product launch?
How is your after-sales support performing against your competitors?
What is the general sentiment about the election manifestos of various political parties?
These are questions one asks to learn the opinion of people, track keywords, analyse opinion and converting text to numerical ratings.
You may ask, why are opinions important?
While individuals seek opinions from friends and family, organizations use surveys, user groups, opinion polls, consultants and social media.
From where is opinion or sentiment mined?
Basic search commands used for analysis:
So how would you define sentiment analysis?
Sentiment analysis is the computational study of opinions, sentiments, evaluations, attitudes, appraisal, views, emotions, and any other content with subjectivity expressed in text form. It looks further the traditional Social KPIs (number of mentions, followers, etc.) to process language and text contained within social data; reviews, blogs, online discussions, news, comments, feedback, or other documents; using variables such as context, tone and emotion,
How does Sentiment Analysis work?
Sentiment analysis applies certain pre-defined methods to classify the polarity of a post, comment or statement, analysing whether sentiment around a topic is positive, negative or neutral. Proprietary tools are used or in-house models of social media monitoring software are developed – using statistics and natural language processing (NLP) techniques to automate sentiment analysis on vast amounts of social data.
The predictive character of sentiment analysis
As social data is unsolicited and user generated, analysing sentiments in online conversations with respect to specific topics help predicting outcomes, whether a political event, marketing campaign, product launch, etc. For example, sentiment reports pulled from Twitter around the Indian elections helps identify sentiments of the electorate and predict results.While social conversations in digital space generate insights for product positioning.
Application areas of Sentiment Analysis
Sentiment Analysis and social data enable business decision makers to understand consumer attitudes and behaviors more than ever before. Since social data is unsolicited and user generated, mining this data and categorizing it allows companies to understand intelligence around customers’ feelings towards campaigns, content, or products.
Sentiment analysis = Opinion mining = Sentiment mining = Subjectivity analysis = Affect analysis = Emotion detection = Opinion spam detection
More from IVY Blogs: