Sentiment classification of skewed shoppers' reviews using machine learning techniques, examining the textual features

Abstract

With the speedy growth of online shopping, it has become of crucial importance for product makers to analyze, and handle a wealth of products' reviews. However, such a high volume of reviews, along with a wide variety of opinions, makes it hard for manufacturers to know exactly how they can improve their products without having an efficient approach. For this purpose, the results of sentiment classification would help the customers to retrieve the necessary information to choose an appropriate product, and the sellers to effectively collect customer feedback in order to improve their products. Like most of the read‐world problems, the shopping review data being used in this study were imbalanced, being predominately composed of positive with only a small percentage of negative reviews. Machine learning (ML) algorithms do not perform well when data are imbalanced, as they tend to get biased toward the overrepresented data category. The synthetic minority over‐sampling technique (SMOTE) was used to address this class imbalance problem. In this study, three different ML‐based algorithms, namely the Naïve Bayes (NB), Support Vector Machine, and decision tree (DT) were employed. An extensive preprocessing procedure was taken to prepare the text datasets, and details are discussed in the manuscript. The performance analysis indicated that the DT algorithm outperforms the other two methods. As positive reviews account for the majority of the reviews, sparse words removal for the data resulted in the removal of almost all negative reviews' sentiments. Hence, the model training process is here performed on positive and negative reviews separately. A combination of the review titles with their contents, separate tokenization process, applications of various N‐gram, and maintaining stops words (e.g. “not” or “but”) were some other steps considered to improve the performance of the model.

Tourist Reviews Sentiment Classification using Deep Learning Techniques: A Case Study in Saudi Arabia

“…In light of this, unstructured data from social media has increased the demand for sentiment analysis. The author in [13] explained that sentiment analysis could be at different levels, such as document level, sentence level, and aspect-based. The results of experimental studies [14] also showed that one of the tasks of sentiment classification at the document level is to consider the importance of sentences.…”

Meta-Learner for Amharic Sentiment Classification

“…After applying the Grid Search algorithm to the TF-IDF vectorizer implemented in the Scikit learn Python package [53], the TF-IDF character (1,7) grams feature set and maximum features of 5000 has been chosen for optimal Amharic sentiment classification. For several NLP applications, the TF-IDF character level n-grams feature outperforms the word-level grams feature, according to the literature [25,29,54]. Specifically, character n-grams features outperform word grams features for dealing negation in Amharic sentiment classification [22].…”

“…(3) Base Learner Algorithms: Support Vector Machine (SVM), Naive Bayesian (NB), and Random Forest (RF) are the most commonly used supervised machine learning algorithms in NLP [54,56], and they were chosen for Amharic sentiment classification in this study. For the sake of simplicity and comparison, we choose Logistic Regression (LR) as a meta-learner for combining the base learners.…”

Sentiment Analysis Using Treebank Filtered Preprocess with Relevant Vector Boost Classifier

“…But, the designed method failed to achieve higher prediction accuracy. Several machine learning approaches were designed in [14] for Sentiment classification using examining the reviews. A novel pattern-based method was introduced in [15] for aspect extraction and sentiment analysis with higher accuracy.…”