ARTFSC Average Relative Term Frequency Sentiment Classification

Authors

  • Kranti Vithal Ghag MET’s SAKEC, Mumbai University,Mumbai.
  • Ketan Shah SVKM’s NMIMS MPSTME, Mumbai, India.

DOI:

https://doi.org/10.24297/ijct.v12i6.3141

Keywords:

Sentiment Classification, Term Weighting, Term Frequency, Term Presence, Document Vectors.

Abstract

Sentiment Classification refers to the computational techniques for classifying whether the sentiments of text are positive or negative. Statistical Techniques based on Term Presence and Term Frequency, using Support Vector Machine are popularly used for Sentiment Classification. This paper presents an approach for classifying a term as positive or negative based on its average frequency in positively tagged documents in comparison with negatively tagged documents. Our approach is based on term weighting techniques that are used for information retrieval and sentiment classification. It differs significantly from these traditional methods due to our model of logarithmic differential average term distribution for sentiment classification. Terms with nearly equal distribution in positively tagged documents and negatively tagged documents were classified as a Senti-stop-word and discarded. The proportional distribution of a term to be classified as Senti-stop-word was determined experimentally. Our model was evaluated by comparing it with state of art techniques for sentiment classification using the movie review dataset.

Downloads

Download data is not yet available.

Author Biographies

Kranti Vithal Ghag, MET’s SAKEC, Mumbai University,Mumbai.

Information Technology Department

Ketan Shah, SVKM’s NMIMS MPSTME, Mumbai, India.

Information Technology Department

Downloads

Published

2014-02-14

How to Cite

Ghag, K. V., & Shah, K. (2014). ARTFSC Average Relative Term Frequency Sentiment Classification. INTERNATIONAL JOURNAL OF COMPUTERS &Amp; TECHNOLOGY, 12(6), 3591–3601. https://doi.org/10.24297/ijct.v12i6.3141

Issue

Section

Research Articles