R eview C raft : A W ord2 V ec Driven System Enhancing User-Written Reviews (2023)

Abstract: The significance of online product reviews has become indispensable for customers in making informed buying decisions, while e-commerce platforms use them to fine tune their recommender systems. However, since review writing is purely a voluntary process without any incentives, most customers opt out from writing reviews or write poor-quality ones. This lack of engagement poses credibility issues as fake or biased reviews can mislead buyers who rely on them for informed decision-making. To address this issue, this paper introduces a system that suggests product features and appropriate sentiment words to help users write informative product reviews in a structured manner. The system is based on Word2Vec model and Chi square test. The evaluation results demonstrates that the reviews with recommendations showed a 2 fold improvement both, in the quality of the features covered and correct usage of sentiment words, as well as a 19% improvement in overall usefulness compared to reviews without recommendations. Keywords: Word2Vec, Chi-square, Sentiment words, Product Aspect/Feature.

## introduction in the current digital era, customers view product reviews over the internet as a vital resource in their buying process @xcite . the increase in the number of online products reviews makes it difficult for consumers to search for genuine and valuable information concerning a particular product. the companies are also held accountable by publicly shared reviews, which in turn prompt them to offer better products and services to maintain a good brand image @xcite . but there are many poor quality review that do not provide useful information, are biased and bad written therefore potential buyer cannot use these review to make informed decision to buy. this issue has led to a growing need for an advanced review system that can motivate more people to write good quality product reviews. one of the primary challenges in review writing is the difficulty in articulating specific product features and their associated sentiment. for instance a customer may wish to comment on how bad the camera of a mobile phone is but they might not be aware of the technical words and features associated with camera specifications. for example, when expressing dissatisfaction with the camera, a customer may simply state that "the camera of this model is bad". although the sentiment is conveyed here, this sentences is not of much use to other users since it is not really conveying the issues that make the camera bad. most of the reviews are filled with such generic sentences. however, if a customer is able to pinpoint the specific problems that make the camera bad, for example, "the camera is of low megapixels" or " the night photography is bad", these details in the reviews can make the reviews submitted by the customers much more usable than the generic review, but most laymen users are not aware of such features or may not remember the intricacies when writing reviews, especially since review writing is often done as a casual activity. besides this, the sentiment words may not convey similar meaning when they are used with different features, thus leading to ambiguity. for example, the word "slow" in "slow battery drain" might signify that the phone's battery is long lasting but it will be a negative sentiment if it is referring to the "processing speed". to address these challenges, this paper proposes a novel approach that is based on word2vec model @xcite and chi-square test @xcite , to assist users in writing more effective and valuable reviews. this paper is based on @xcite where authors propose a review writing recommender system based on lda model to extract features, our approach extends beyond this method. unlike the lda model, which falls short in finding similar words or examining words at a more detailed level, we utilize the word2vec model to extract relevant product features. the identification of feature-specific sentiment words in this paper is based on @xcite . these words are subsequently presented to users in a structured manner, ensuring an enhancement in both the quantity and quality of information within the reviews. thus it provides potential consumers with an extra basis for choosing rightly. the rest of the paper is organized as follows: section 2 provides a literature review of existing work on product reviews and recommender systems. section 3 describes the proposed system and its components in detail. section 4 provides the details of the experiment and the dataset used. section 5 presents the results obtained. finally, section 6 concludes the paper and highlights future direction. ## literature survey there has been a significant amount of research in the fields of social networking and e-commerce, specifically in the areas of natural language processing (nlp) and machine learning (ml) algorithms. @xcite presents ghostwriter-2.0, a case-based reasoning system that suggests topics for new product reviews based on existing reviews. @xcite introduced a tool called reviewer's assistant which focuses on the development of the browser plugin as a tool to assist users in writing better quality reviews. the study suggests a unique methodology exploiting association rule mining for extracting relevant product attributes based on the content being typed by the user. @xcite describes practical implementation and assessment for a browser plugin. a user study was then carried out by the researchers to assess how useful the plugin could be in improving the quality of the reviews generated by users. @xcite proposes a system which uses unsupervised topic extraction with the lda algorithm to suggest relevant topics for users writing product reviews. another study @xcite conducted a comparative analysis of various methods used in literature for extracting features from user reviews. (bhagat and pawar, 2020) presents a method to extract features using latent dirichlet algorithm (lda), this study can be useful for grouping related words into topics and extracting features, it may not be as effective for finding similar words or performing word-level analysis. @xcite applies the chi-square test statistical measure to determine the polarity of sentiment words by examining their occurrence in positive and negative reviews. while this work focuses on polarity differences at the domain level, it does not explore potential changes in polarity at the feature level. ## methodology the proposed work aims to extract aspects and aspect-based polarity of sentiment words in user reviews. the methodology involves two key steps: aspect extraction using word2vec model and finding the aspect-based polarity of the sentiment word using the chi-square test statistical measure. the word2vec is a neural network-based model that generates word embeddings, representing words as vectors in a high-dimensional space. this allows for mathematical operations like addition and subtraction, enabling the system to capture the semantic meaning of words and their relationships. for example, if we wanted to find a word that is similar to "iphone" we could perform the operation such as ## implementation details and dataset used the implementation of the experiment is done in python programming language @xcite . for text processing tasks, we utilize the natural language toolkit (nltk) library @xcite . part-of-speech tagging is performed using the spacy library (srinivasa-desikan, 2018). the gensim library is employed to train a word2vec model on pre-processed review text. the dataset employed in our experiment consists of reviews sourced from amazon.com @xcite . specifically, we conduct our tests on the dataset pertaining to cell phones and its related accessories we conducted an experiment on a group of 29 students. these students were requested to write reviews about the mobile phones they currently use, on a website we created, sharing their levels of satisfaction or dissatisfaction. the students wrote reviews in the first phase without a recommendation system. in the second stage, the students again wrote another review, although they were provided with an automated recommendation system that provided guidance about the features and sentiment words that they could incorporate in the review as shown in figure 1 . two human judges evaluated the quality of every student's review. the judges were provided with three parameters: 1) the quality of features covered, 2) the correct usage of sentiment words to describe the features and 3) the overall usefulness of the review. the reviewers used a three-point scale: i.e. 1 -poor, 2 -average and 3 -good to rate the reviews. the fleiss's kappa inter-rater reliability measure was used to monitor how much agreement existed between the judges. the fleiss kappa was interpreted using the landis and koch criteria with kappa value less then 0 indicating poor agreement, 0.01-0.20 slight agreement, 0.21-0.40 fair agreement, 0.41-0.60 moderate agreement, 0.61-0.80 substantial agreement and 0.81-1.00 indicating almost perfect agreement @xcite . ## results noun adjective sentiment reset hard negative phones smart neutral phones worst negative phones good positive phones best positive phones bad negative phones better neutral figure 2 indicates the average of the ratings by judge 1 and judge 2. the average of the ratings given by both the judges shows that out of 29, 6 reviews were reported to be of quality in terms of quality of features mentioned. we can see from the graph that after the use of the system, the number increased to 11. similarly, for correct use of sentiment words, without the use of any help, only in 4 reviews without recommendations reviews with recommendations quality of features covered 0.4423 0.5250 correct usage of sentiment words to describe the feature 0.3614 0.5816 overall usefulness of the review 0.3088 0.4276 reviews without recommendations reviews with recommendations the phone is good for many apps, but sometimes the camera gets stuck. battery is not good. but it has a good zoom to take close pictures. the phone is optimized for handling multiple apps, but the camera is laggy, it also has nice zoom feature for closeups. charging speed is decent but drain quickly barely lasts a day. the audio quality for calls is clear and loud. this is a great phone i have been using it for 4 years. the camera quality is also great. the only issue is the video lags a lot.overall for such a cheap rate the phone is pretty good. it is great phone which has been used for very long time. so now there is some issue with the phone while gaming streaming as the phone starts heating up which is pretty frustrating. it is pretty heavy so it will be pretty good if you guys make lighter phones. its the best purchase i have done. it has been there with me for more two years now and still working as i have brought new. just you might have the problem with the battery . its overall good phone to purchase. i would recommend to buy its as you get most of the feature in cheap price. camera megapixels is excellent. the back camera is great the display is decent. the speakers are durable. the phone might you feel heavy and bulky.the overall features and capabilities are good at this price . the phone is very slim and handy. the fingerprint response to unlock the phone is fast takes about 2-3 secs. if the phone has lot of apps installe on it the battery won't last for long. it has a very user friendly ui i had this phone for the last 4 years its a g8 budget phone, both the front and rear cameras are wonderful, sound quality is quite good, display quality is very good but the saturation is on the higer side, battery is decent with 4000mah, looks very sleek and is also very light gives a premium feel, but the box doesnt contain earphone, and also the sim tray will either support one sim and a sd-card or two sims. ## conclusion good quality product reviews play a vital role in establishing a robust ecommerce system. they enable customers to express their opinions effectively and ensure that their voices are acknowledged and addressed. this research introduces a promising approach to help users write informative reviews confidently and efficiently, improving the overall quality of product reviews on e-commerce websites. the word2vec model helps to suggest other related features by learning from the context of the user reviews. use of the chi-squared test helps to filter out a lot of words with low sentiment intensity by assigning the sentiment orientations to words whose chi squared value is above a particular threshold. the evaluation results indicate a no