Prediction of Helpful Reviews Using Emotions Extraction

Abstract

Reviews keep playing an increasingly important role in the decision process of buying products and booking hotels. However, the large amount of available information can be confusing to users. A more succinct interface, gathering only the most helpful reviews, can reduce information processing time and save effort. To create such an interface in real time, we need reliable prediction algorithms to classify and predict new reviews which have not been voted but are potentially helpful. So far such helpfulness prediction algorithms have benefited from structural aspects, such as the length and readability score. Since emotional words are at the heart of our written communication and are powerful to trigger listeners' attention, we believe that emotional words can serve as important parameters for predicting helpfulness of review text. Using GALC, a general lexicon of emotional words associated with a model representing 20 different categories, we extracted the emotionality from the review text and applied supervised classification method to derive the emotion-based helpful review prediction. As the second contribution, we propose an evaluation framework comparing three different real-world datasets extracted from the most well-known product review websites. This framework shows that emotion-based methods are outperforming the structure-based approach, by up to 9%.

Multi-perspective Coherent Reasoning for Helpfulness Prediction of Multimodal Reviews

“…and deep learning based methods (Chen et al, 2019;Fan et al, 2018;Chen et al, 2018). The machine learning based methods employ domain-specific knowledge to extract a variety of hand-crafted features, such as structure features (Kim et al, 2006), lexical features (Krishnamoorthy, 2015), emotional features (Martin and Pu, 2014), and argument features (Liu et al, 2017), from the textural reviews, which are then fed into conventional classifiers such as SVM (Kim et al, 2006) for helpfulness prediction. These methods rely heavily on feature engineering, which is time-consuming and labor intensive.…”

“…Motivated by the demand of gleaning insights from such valuable data, review helpfulness prediction has gained increasing interest from both academia and industry communities. Earlier review helpfulness prediction methods rely on a wide range of handcrafted features, such as semantic features (Yang et al, 2015), lexical features (Martin and Pu, 2014), and argument based features (Liu et al, 2017), to train a classifier. The success of these methods generally relies heavily on feature engineering which is labor-intensive and highlights the weakness of conventional machine learning methods.…”

Ranking Online Reviews Based on Their Helpfulness: An Unsupervised Approach

“…The relationship between helpfulness of online reviews and emotions have been explored by Malik and Hussain (2017) where they stud-ied which emotions are important for helpfulness prediction. Martin and Pu (2014) used emotions to detect helpful reviews by applying different classification models (i.e., SVM, Random Forest, and Naïve Bayes) and demonstrated that their approach outperformed methods using POS tagging features. Emotion information has not been considered by any of the existing unsupervised methods.…”

“…In majority of the existing work, supervised machine learning models have been employed considering the problem as a predictive task (i.e. predict whether/how useful a review is) (Martin and Pu, 2014;Krishnamoorthy, 2015;Malik and Hussain, 2017;Singh et al, 2017;Wu et al, 2017;Enamul Haque et al, 2018;Alsmadi et al, 2020). With supervised approaches, various types of features such as linguistic features (Krishnamoorthy, 2015;Malik and Hussain, 2017;Wu et al, 2017) or textual features (i.e.…”

“…Inspired by the work proposed in (Martin and Pu, 2014), Woloszyn et al (2017) developed MRR (Most Relevant Review), a novel unsupervised algorithm to rank reviews based on their estimated relevance. MRR algorithm consists of three steps:…”