Sentiment Analysis

List of Articles Sentiment Analysis

Open Access Article
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1 - An Improved Sentiment Analysis Algorithm Based on Appraisal Theory and Fuzzy Logic
Azadeh Roustakiani Neda Abdolvand Saeideh Rajaei Harandi
10.7508/jist.2018.02.004

Millions of comments and opinions are posted daily on websites such as Twitter or Facebook. Users share their opinions on various topics. People need to know the opinions of other people in order to purchase consciously. Businesses also need customers’ opinions and big More

Millions of comments and opinions are posted daily on websites such as Twitter or Facebook. Users share their opinions on various topics. People need to know the opinions of other people in order to purchase consciously. Businesses also need customers’ opinions and big data analysis to continue serving customer-friendly services, manage customer complaints and suggestions, increase financial benefits, evaluate products, as well as for marketing and business development. With the development of social media, the importance of sentiment analysis has increased, and sentiment analysis has become a very popular topic among computer scientists and researchers, because it has many usages in market and customer feedback analysis. Most sentiment analysis methods suffice to split comments into three negative, positive and neutral categories. But Appraisal Theory considers other characteristics of opinion such as attitude, graduation and orientation which results in more precise analysis. Therefore, this research has proposed an algorithm that increases the accuracy of the sentiment analysis algorithms by combining appraisal theory and fuzzy logic. This algorithm was tested on Stanford data (25,000 comments on the film) and compared with a reliable dictionary. Finally, the algorithm reached the accuracy of 95%. The results of this research can help to manage customer complaints and suggestions, marketing and business development, and product testing. Manuscript profile
Open Access Article
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2 - A Hybrid Machine Learning Approach for Sentiment Analysis of Beauty Products Reviews
Kanika Jindal Rajni Aron
10.52547/jist.15586.10.37.1

20.1001.1.23221437.2022.10.37.1.1

Nowadays, social media platforms have become a mirror that imitates opinions and feelings about any specific product or event. These product reviews are capable of enhancing communication among entrepreneurs and their customers. These reviews need to be extracted and an More

Nowadays, social media platforms have become a mirror that imitates opinions and feelings about any specific product or event. These product reviews are capable of enhancing communication among entrepreneurs and their customers. These reviews need to be extracted and analyzed to predict the sentiment polarity, i.e., whether the review is positive or negative. This paper aims to predict the human sentiments expressed for beauty product reviews extracted from Amazon and improve the classification accuracy. The three phases instigated in our work are data pre-processing, feature extraction using the Bag-of-Words (BoW) method, and sentiment classification using Machine Learning (ML) techniques. A Global Optimization-based Neural Network (GONN) is proposed for the sentimental classification. Then an empirical study is conducted to analyze the performance of the proposed GONN and compare it with the other machine learning algorithms, such as Random Forest (RF), Naive Bayes (NB), and Support Vector Machine (SVM). We dig further to cross-validate these techniques by ten folds to evaluate the most accurate classifier. These models have also been investigated on the Precision-Recall (PR) curve to assess and test the best technique. Experimental results demonstrate that the proposed method is the most appropriate method to predict the classification accuracy for our defined dataset. Specifically, we exhibit that our work is adept at training the textual sentiment classifiers better, thereby enhancing the accuracy of sentiment prediction. Manuscript profile
Open Access Article
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3 - An Efficient Sentiment Analysis Model for Crime Articles’ Comments using a Fine-tuned BERT Deep Architecture and Pre-Processing Techniques
Sovon Chakraborty Muhammad Borhan Uddin Talukdar Portia Sikdar Jia Uddin
10.61186/jist.38322.12.45.1

The prevalence of social media these days allows users to exchange views on a multitude of events. Public comments on the talk-of-the-country crimes can be analyzed to understand how the overall mass sentiment changes over time. In this paper, a specialized dataset has More

The prevalence of social media these days allows users to exchange views on a multitude of events. Public comments on the talk-of-the-country crimes can be analyzed to understand how the overall mass sentiment changes over time. In this paper, a specialized dataset has been developed and utilized, comprising public comments from various types of online platforms, about contemporary crime events. The comments are later manually annotated with one of the three polarity values- positive, negative, and neutral. Before feeding the model with the data, some pre-processing tasks are applied to eliminate the dispensable parts each comment contains. In this study, A deep Bidirectional Encoder Representation from Transformers (BERT) is utilized for sentiment analysis from the pre-processed crime data. In order the evaluate the performance that the model exhibits, F1 score, ROC curve, and Heatmap are used. Experimental results demonstrate that the model shows F1 Score of 89% for the tested dataset. In addition, the proposed model outperforms the other state-of-the-art machine learning and deep learning models by exhibiting higher accuracy with less trainable parameters. As the model requires less trainable parameters, and hence the complexity is lower compared to other models, it is expected that the proposed model may be a suitable option for utilization in portable IoT devices. Manuscript profile

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List of Articles Sentiment Analysis