Using Opinionated-Objective Terms to Improve Lexicon-Based Sentiment Analysis

Sentiment analysis (SA) has received huge attention to understand customer perception, especially in the movie review (IMDB) domain. This is due to the availability of large, labelled dataset. This has enhanced the use and development of machine learning (ML) algorithms ranging from the traditional machine learning algorithms, deep learning algorithms to large language models. The ML models have shown great performances. However, the application of ML methods for SA is limited in service industry like banking, due to the unavailability of large training dataset. Thus, we consider the use of lexicon-based sentiment analysis appropriate. We employ 346,000 Nigeria bank customers’ tweets to develop our corpus and thus, propose SentiLeye, a novel lexicon-based algorithm for sentiment analysis. Our algorithm incorporates corpus-based approach and external lexical resources for sentiment lexicon generation of Pidgin English language terms (a non-English under resourced language). Moreover, we demonstrate the use of verbs and adverbs that express opinion on service experience to improve the performance of lexicon-based sentiment analysis. Results show that SentiLeye outperforms popular off-the-shelf sentiment lexicons with macro F1-score of 76%. We conclude that results from domain specific algorithms such as SentiLeye evidence that general-purpose lexicons cannot replace them.