An N-Gram Feature-Based Sentiment Classification Model for Drug User Reviews

Nowadays, there are several websites that allow customers to buy and post reviews of purchased products, which results in incremental accumulation of a lot of reviews written in natural language. Moreover, conversance with E-commerce and social media has raised the level of sophistication of online shoppers and it is common practice for them to compare competing brands of products before making a purchase. Prevailing factors such as availability of online reviews and raised end-user expectations have motivated the development of opinion mining systems that can automatically classify and summarize users’ reviews. This paper proposes an opinion mining system that can be used for both binary and fine-grained sentiment classifications of user reviews. Feature-based sentiment classification is a multistep process that involves preprocessing to remove noise, extraction of features and corresponding descriptors, and tagging their polarity. The proposed technique extends the feature-based classification approach to incorporate the effect of various linguistic hedges by using fuzzy functions to emulate the effect of modifiers, concentrators, and dilators. Empirical studies indicate that the proposed system can perform reliable sentiment classification at various levels of granularity with high average accuracy of 89% for binary classification and 86% for fine-grained classification.

Download Full-text

Google Play Content Scraping and Knowledge Engineering using Natural Language Processing Techniques with the Analysis of User Reviews

Journal of Intelligent Systems ◽

10.1515/jisys-2019-0197 ◽

2020 ◽

Vol 30 (1) ◽

pp. 192-208 ◽

Cited By ~ 1

Author(s):

Hamza Aldabbas ◽

Abdullah Bajahzar ◽

Meshrif Alruily ◽

Ali Adil Qureshi ◽

Rana M. Amir Latif ◽

...

Keyword(s):

Logistic Regression ◽

Language Processing ◽

Mobile Application ◽

Knowledge Engineering ◽

Machine Learning Algorithms ◽

Application Development ◽

User Reviews ◽

N Gram ◽

Logistic Regression Algorithm ◽

Google Play

Abstract To maintain the competitive edge and evaluating the needs of the quality app is in the mobile application market. The user’s feedback on these applications plays an essential role in the mobile application development industry. The rapid growth of web technology gave people an opportunity to interact and express their review, rate and share their feedback about applications. In this paper we have scrapped 506259 of user reviews and applications rate from Google Play Store from 14 different categories. The statistical information was measured in the results using different of common machine learning algorithms such as the Logistic Regression, Random Forest Classifier, and Multinomial Naïve Bayes. Different parameters including the accuracy, precision, recall, and F1 score were used to evaluate Bigram, Trigram, and N-gram, and the statistical result of these algorithms was compared. The analysis of each algorithm, one by one, is performed, and the result has been evaluated. It is concluded that logistic regression is the best algorithm for review analysis of the Google Play Store applications. The results have been checked scientifically, and it is found that the accuracy of the logistic regression algorithm for analyzing different reviews based on three classes, i.e., positive, negative, and neutral.

Download Full-text

Improving Sentiment Classification Accuracy of Financial News Using N-Gram Approach and Feature Weighting Methods

2015 2nd International Conference on Information Science and Security (ICISS) ◽

10.1109/icissec.2015.7371008 ◽

2015 ◽

Cited By ~ 4

Author(s):

S. Foroozan ◽

M. A. Azmi Murad ◽

N. M. Sharef ◽

A. R. Abdul Latiff

Keyword(s):

Classification Accuracy ◽

Feature Weighting ◽

Sentiment Classification ◽

Financial News ◽

N Gram ◽

Weighting Methods

Download Full-text

Chinese Sentiment Classification Model based on Pre-Trained BERT

2021 2nd International Conference on Computers, Information Processing and Advanced Education ◽

10.1145/3456887.3457511 ◽

2021 ◽

Author(s):

Jie Gao

Keyword(s):

Sentiment Classification ◽

Classification Model ◽

Model Based

Download Full-text

A Variational Point Process Model for Social Event Sequences

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i01.5348 ◽

2020 ◽

Vol 34 (01) ◽

pp. 173-180

Author(s):

Zhen Pan ◽

Zhenya Huang ◽

Defu Lian ◽

Enhong Chen

Keyword(s):

Point Process ◽

Real World ◽

Process Model ◽

Classification Model ◽

Event Sequence ◽

Event Sequences ◽

Sequence Modeling ◽

Stochastic Point Process ◽

Feature Based ◽

Real World Datasets

Many events occur in real-world and social networks. Events are related to the past and there are patterns in the evolution of event sequences. Understanding the patterns can help us better predict the type and arriving time of the next event. In the literature, both feature-based approaches and generative approaches are utilized to model the event sequence. Feature-based approaches extract a variety of features, and train a regression or classification model to make a prediction. Yet, their performance is dependent on the experience-based feature exaction. Generative approaches usually assume the evolution of events follow a stochastic point process (e.g., Poisson process or its complexer variants). However, the true distribution of events is never known and the performance depends on the design of stochastic process in practice. To solve the above challenges, in this paper, we present a novel probabilistic generative model for event sequences. The model is termed Variational Event Point Process (VEPP). Our model introduces variational auto-encoder to event sequence modeling that can better use the latent information and capture the distribution over inter-arrival time and types of event sequences. Experiments on real-world datasets prove effectiveness of our proposed model.

Download Full-text