Abstract
Now-a-days, it is very common that the customers share their thoughts about any product, brand and their experience in social media. The analysts collect these reviews and process it, to extract meaningful information about the product. The beauty of social media is, it’s involved in all the domains. So the analysts got reviews from different social media and platforms for almost all kind of thing. The Sentiment Analysis is applied to predict outcomes for getting useful information, for ex.; like predict the blockbuster for a movie, rating for any new launches and many more. This type of prediction is really helpful for the customer to buy any goods or take any services in this competitive world. This paper is focused on e-commerce website reviews which are normally in text form with some special characters and some symbols (emojis). Each word in this text set got some meaning in terms of context, emotion and prior experience. These characteristics contribute to some of the features of text data for prediction. The objective of this paper is to compile existing research works on text analysis and emotion based analysis. The open issues and challenges of document based sentiment analysis are also discussed. The paper concluded with proposing a new approach of multi class classification. Ternary classification for classes positive, negative and neutral is suggested primarily for product based text and emoji reviews on Twitter social media.
Highlights
The Sentiment Analysis is one of the Natural LanguageProcessing (NLP) techniques that used to find opinion, whether data is positive, negative or neutral
This paper considers twitter as a social media platform to collect these opinions, primary cause is it is popular these days and second cause is, as it limits the review to 140 characters, it is precise and can be useful for effective analysis
As shown in Fig-01, machine learning consists of a set of algorithm that can be applied for social media text data for sentiment analysis
Summary
Processing (NLP) techniques that used to find opinion, whether data is positive, negative or neutral. First step in sentiment analysis is to collect the opinions for a particular product from the customers. The reviews are mostly in text form, which is unstructured This information contains some general words that can classify our opinion into different classes. Sometime customers use some special symbols to write review, these symbols have specific meaning These symbols are to be converted into text, for processing and classification into particular opinion class is done and in turn these reviews generate accurate prediction [4]. Machine Learning methods are mainly used in sentimental analysis. As shown in Fig-01, machine learning consists of a set of algorithm that can be applied for social media text data for sentiment analysis.
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