Enhancement of the Lexical Approach by N-Grams Technique via Improving Negation-Based Traditional Sentiment Analysis


  • Harish Dutt Sharma Department of Computer Application, School of CA&IT, Shri Guru Ram Rai University, Dehradun 248001, Uttarakhand, India
  • Sanjay Sharma Department of Computer Application, School of CA&IT, Shri Guru Ram Rai University, Dehradun 248001, Uttarakhand, India


Negation handling, N-Grams techniques, Sentiment analysis, Pre-processing technique, Machine learning


Sentiment analysis, often known as opinion mining, is a significant area in artificial intelligence today. Sentiment analysis was widely observed in this field. Currently, a lot of data is constantly being exchanged as text on social networking and e-commerce platforms like Facebook, Twitter, Amazon, etc. Therefore, sentiment analysis is the best technique for businesses to comprehend what their customers want from them so that they may adapt their plans in response to client feedback and expand their customer base. To extract the exact meaning from the text is a tough task. So here, our effort is to get the positive and negative sentiment of reviews from the dataset and enhance the performance of sentiment through Natural language processing (NLP) over pre-existing pre-processing technique and machine learning algorithms. So for this purpose, we have an Amazon product review dataset. Which is an extract from the Kaggle website. In this study, we aim to remove noise from the dataset and improve the traditional NLP preprocessing technique after that, we will use Term Frequency-Inverse Document Frequency (TF-IDF) method for feature selection and then classify the result through the classification algorithm such as Artificial Neural Network (ANN), Naïve Bayes (NB), and Support Vector Machine (SVM).


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How to Cite

Sharma, H. D., & Sharma, S. . (2024). Enhancement of the Lexical Approach by N-Grams Technique via Improving Negation-Based Traditional Sentiment Analysis. International Journal of Intelligent Systems and Applications in Engineering, 12(15s), 63–69. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4715



Research Article