Impact of Textual Length on Lexicon-Based Sentiment Classification for Hindi Reviews

The performance of sentiment analysis is affected by different properties like dataset size or training corpus size, feature extraction, length of target documents, preprocessing constraints, length of the review/word count, readability, purity of data, subjectivity of data, etc. Efforts need to be taken to not only increase the performance of the algorithms but also to investigate the impact of some of the linguistic properties of the dataset on the performance. One such data property is word count. The lexicon model was experimented for effect of word count and was tested on both the single and multi-domain datasets to find the effect of word count on accuracy and other parameters by splitting into four categories, where each category or group had a different word count. The experimentation results indicated that the accuracy was significantly high for sentences that had shorter words. After a particular threshold, the accuracy starts decreasing as the number of words in the sentence goes on increasing. The statistical significance of the impact of word count on the classification accuracy clearly indicates that the impact of the review words count on the classification accuracy is statistically significant at a 99% confidence level. Less than 80 words is an ideal level for the review words count.