Recognition and Effective Handling of Negations in Enhancing the Accuracy of Urdu Sentiment Analyzer

Abstract

Although work has been done in Urdu Sentiment Analysis by researchers but still there is a lot of room for improvement in the form of achieving higher accuracy. Therefore, in this research, the accuracy of Urdu Sentiment Analysis in multiple domains is enhanced by dealing negations using Lexicon-based approach, one of the broadly used approaches for performing Sentiment Analysis. Negations in Urdu Sentiment Analysis are particularly focused in this research because of their effective role in Sentiment Analysis. Both local and long distance negations are considered. For achieving this goal, a corpus with 6025 Urdu sentences, from 151 blogs that belong to 14 different genres is taken in which use of negations is carefully observed. Two major steps are taken in this regard. First, to deal with the morphological negations, this type of negations is included in the negative word file of the Urdu Sentiment Lexicon developed for performing Sentiment Analysis of Urdu blogs. Secondly, rule-based approach is used for handling the implicit and explicit negations. Rules are designed that can deal with both implicit and explicit negations effectively. Implementation of these rules increased the accuracy of Sentiment Analyzer from 73.88% to 78.32% with 0.745, 0.788 and 0.745 Precision, Recall and Fmeasure respectively, which is statistically significant improvement.

Sentiment Analysis of Roman Text: Challenges, Opportunities, and Future Directions

“…In summary, by effectively dealing with negations, particularly considering both local and distance negations, the accuracy of Urdu SA in various domains can be significantly enhanced. The rule-based approach applied in this research proved successful, leading to notable improvements in accuracy and performance metrics [31].…”

Cognitive Relationship-Based Approach for Urdu Sarcasm and Sentiment Classification

“…The sentiment analyzer achieves an accuracy of 83.4%, and this improves by 5.09% with the use of intensifiers. Another study by Mukhtar et al [23] investigated the impact of negations in the USA, using the same dataset as their earlier work on identification [22]. However, this time, with an overall accuracy of 78.32%, the improvement is seen around 4.4% when the negations are handled during the analysis.…”

Innovations in Urdu Sentiment Analysis Using Machine and Deep Learning Techniques for Two-Class Classification of Symmetric Datasets

“…People's feelings and behavior toward a product/service or brand has gained importance in the field of opinion mining, therefore opinion mining on Urdu comments on websites was performed [16]. A rule-based approach was used to handle negations in Urdu sentences, thus improving the accuracy of S.A [17]. A word-level translation scheme was introduced to create an Urdu lexicon as well as polarity scores to assign sentiments and performed evaluation [18].…”