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With the development of web technologies, an increasing amount of opinions are published online every day!!
People rely on the reviews more than before to help determine the quality of product in which they are interested!!
We use the chatter data from Twitter.com to forecast box-office revenues for movies!!
The data that is being generated on the social networks as a result of human activities is commonly referred as Social Data. This is one of the largest streams of big data. The response observed during the Facebook IPO clearly suggest that social networking websites are going to witness unprecedented growth in the near future.
It is widely observed that these websites have become popular media for people to share their opinions, perceptions, attitudes, judgments, as well as personal happenings. Similarly, enterprises are leveraging this data for tracking their customers, analyzing their habits & behavior, marketing their products, measuring their competitive advantage, and improving customer relationship.
The rapid pace at which the social data is growing has made it critical for enterprises to unlock customer sentiment embedded in this big data stream. It is proposed that this will help them respond to customer complaints, improve their product quality. This highlights the importance of the field of Sentiment Analysis.
In plain terms, Sentiment Analysis is the extraction of linguistic and subjective information of opinions, attitudes, emotions, and perspectives. This can be further used to develop predictive models that can outperform market-based indicators. For example, a statistical model to predict the performance of movies at box-office can be developed by conducting Sentiment Analysis on movie reviews. This type of statistical models can considerably relieve the burden on people to self-analyze the huge amounts of reviews available in print and online.
However, Sentiment Analysis is not easy because terms have many context dependent meanings. In addition, most of the data that is currently available on the internet is unstructured and contains huge amount of noise, which needs to filtered-out before the data can be put to use. Another problem that is critically important is the fact that social data is being generated across different geographical locations. It is established in research that a person’s attitude, perceptions and opinion are significantly influenced by his/her environment, culture, religious beliefs, ethnicity and other sociology-economic factors.
Thus, we believe that social data has tremendous value associated with it and it can generate important insights which can be further leveraged for social and economic welfare. The domain of Sentiment Analysis is one of the areas that is set to expand exponentially in future, however, it remains to be seen how the associated hurdles are taken care of.
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