Text Compression for Sentiment Analysis via Evolutionary Algorithms
This addresses the need for efficient text storage and processing in sentiment analysis applications, though it is incremental as it builds on existing compression and sentiment analysis methods.
The study tackled the problem of compressing text for sentiment analysis without losing accuracy, proposing PARSEC, an evolutionary algorithm using Parts-of-Speech tags, which achieved up to 75% data compression with only a 3.3% loss in sentiment classification accuracy using the LingPipe algorithm.
Can textual data be compressed intelligently without losing accuracy in evaluating sentiment? In this study, we propose a novel evolutionary compression algorithm, PARSEC (PARts-of-Speech for sEntiment Compression), which makes use of Parts-of-Speech tags to compress text in a way that sacrifices minimal classification accuracy when used in conjunction with sentiment analysis algorithms. An analysis of PARSEC with eight commercial and non-commercial sentiment analysis algorithms on twelve English sentiment data sets reveals that accurate compression is possible with (0%, 1.3%, 3.3%) loss in sentiment classification accuracy for (20%, 50%, 75%) data compression with PARSEC using LingPipe, the most accurate of the sentiment algorithms. Other sentiment analysis algorithms are more severely affected by compression. We conclude that significant compression of text data is possible for sentiment analysis depending on the accuracy demands of the specific application and the specific sentiment analysis algorithm used.