ACCURACY IN BINARY, TERNARY AND MULTI-CLASS CLASSIFICATION SENTIMENTAL ANALYSIS-A SURVEY

Anjume Shakir, Jyoti Arora

Abstract


Sentiment analysis is nowadays quite a hot topic for research. Since most of the research is been done on the data acquired from the social networking sites mostly twitter and is subsequently classified into binary classification (“positive” and “negative”) or the ternary classification (“positive”, “negative”, and “neutral”). The binary and ternary classification is not going to serve the sole purpose of sentimental analysis. Multi-class classification can help in getting the essence and core message from the data. Whether it is binary, ternary or multi-class classification, the main objective always remains the accuracy of finding the actual sentiments. Since ample work has been done on binary and ternary classification and the better accuracy has been achieved but in case of multi-class classification accuracy is still a challenge. In this paper, we will analyze different machine learning algorithms and techniques that have been used in the sentimental analysis and the accuracy achieved using those algorithms and techniques.

Keywords


Sentiment analysis, twitter, multi-class classification, machine learning.

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DOI: https://doi.org/10.26483/ijarcs.v9i2.5866

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