Abstract
Sentiment analysis is one of the most popular domains for natural language text classification, crucial for improving information extraction. However, massive data availability is one of the biggest problems for opinion mining due to accuracy considerations. Selecting high discriminative features from an opinion mining database is still an ongoing research topic. This study presents a two-stage heuristic feature selection method to classify sports articles using Tabu search and Cuckoo search via Levy flight. Levy flight is used to prevent the solution from being trapped at local optima. Comparative results on a benchmark dataset prove that our method shows significant improvements in the overall accuracy from 82.6% up to 89.5%.
Highlights
The Internet is a rich source of various points of view and an increasing number of individuals are using the Web as a medium for sharing their opinions and attitudes in text
The results indicated comparable outcome between Ant and Cuckoo algorithms, we preferred to proceed with Cuckoo search based on Lévy flights in the stage, as it has quick and efficient convergence, less complexity, easier to implement with a smaller number of parameters compared to PSO, Ant and Bat algorithms (Beheshti and Shamsuddin, 2013; Kamat and Karegowda, 2014)
The nine common features according to the results generated using Cuckoo-Tabu search and the three techniques were the frequencies of foreign words, modal auxiliaries, singular common nouns, pre-determiners, comparative adverbs, base form verbs, WH determiners, first-person pronouns and second-person pronouns (Fig. 3)
Summary
The Internet is a rich source of various points of view and an increasing number of individuals are using the Web as a medium for sharing their opinions and attitudes in text. This includes online product or service reviews, travel advice, social media discussions and blogs, customer recommendations, movie and book reviews and stock market predictions (Zhou et al, 2013). Sentiment analysis, which involves evaluating sentences as objective or subjective, is challenging to interpret natural language as subjectivity needs more investigation (Pang and Lee, 2008). Subjectivity can be expressed in different ways as proposed in (Liu, 2012) and overall, it is considered highly domain-dependent since it is affected by the sentiments of words
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