Sentiment analysis based on text is a multi-disciplinary research area, which involves natural language processing, database, information retrieval, data mining and artificial intellect, etc. The solution of a basic and classic sentiment analysis problem includes following steps:
1) Collect data
Currently, most of researches collect data from blogs, professional comment sites, news sites and some e-commercial sites. Among these products, users comments and blogs are preference for researchers.
2)Pre-processing
Research has been done in finding and telling subjective sentences in plain texts. But most work use existing sentences for classification. However, in order to reduce inference and increase accuracy, pre-processing is necessary.
According to features of different data and algorithms' requirements, pre-processing methods may vary from each other. For ex, stop-words, prefix (or suffix) trimming, part of speech, and necessary simplification and replacement.
3)Extract features
Feature is prerequisite for classification. According to need of classification, the most direct selection is sentimental word, like "Happy", "Good", "Angry" etc. Feature word can be extracted automatically, but sometimes sentiment vocabulary needs manual formulation.
4)Classification
Classification problem are often solved with standard classification algorithm, such as Support Vector Machines, Naive Bayes, etc.
I have a question about extracting features. Is there any way to determine which part in a word can be common features and used in classfication algorithms?
回复删除I am also sharing natural language processing in my blogs, please take a look and further discuss later!
回复删除Nice sharing! Text-based sentiment analysis is simple to implement and efficient, but its disadvantage is not very accurate due to the variety of syntax. :D
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