Aspect-based Sentiment Analysis using Dependency Parsing

In this paper, an aspect-based Sentiment Analysis (SA) system for Hindi is presented. The proposed system assigns a separate sentiment towards the different aspects of a sentence as well as it evaluates the overall sentiment expressed in a sentence. In this work, Hindi Dependency Parser (HDP) is used to determine the association between an aspect word and a sentiment word (using Hindi SentiWordNet) and works on the idea that closely connected words come together to express a sentiment about a certain aspect. By generating a dependency graph, the system assigns the sentiment to an aspect having a minimum distance between them and computes the overall polarity of the sentence. The system achieves an accuracy of 83.2% on a corpus of movie reviews and its results are compared with baselines as well as existing works on SA. From the results, it has been observed that the proposed system has the potential to be used in emerging applications like SA of product reviews, social media analysis, etc.

KnowMIS-ABSA: an overview and a reference model for applications of sentiment analysis and aspect-based sentiment analysis

The analysis of the opinions of customers and users has been always of great interest in supporting decision-making in many fields, especially in marketing. Sentiment analysis (SA) is the umbrella term for techniques and approaches that analyze user’s sentiments, emotions, opinions in text or other media. The need for a better understanding of these opinions paved the way to novel approaches that focus on the analysis of the sentiment related to specific features of a product, giving birth to the field of aspect-based sentiment analysis (ABSA). Although the increasing interest in this discipline, there is still confusion regarding the basic concepts of ABSA: terms like sentiment, affect, emotion, opinion, are used as synonyms while they represent different concepts. This often leads to an incorrect analysis of the users’ opinions.This work presents an overview of the state-of-the-art techniques and approaches for ABSA, highlighting the main critical issues related to current trends in this field. Following this analysis, a new reference model for SA and ABSA, namely the KnowMIS-ABSA model, is proposed. The model is grounded on the consideration that sentiment, affect, emotion and opinion are very different concepts and that it is profoundly wrong to use the same metric and the same technique to measure them. Accordingly, we argue that different tools and metrics should be adopted to measure each of the dimensions of an opinion. A qualitative case study, regarding product reviews, is proposed to motivate the advantages of the KnowMIS-ABSA model.

The Effects of Gender Signals and Performance in Online Product Reviews

This work quantifies the effects of signaling gender through gender specific user names, on the success of reviews written on the popular amazon.com shopping platform. Highly rated reviews play an important role in e-commerce since they are prominently displayed next to products. Differences in reviews, perceived—consciously or unconsciously—with respect to gender signals, can lead to crucial biases in determining what content and perspectives are represented among top reviews. To investigate this, we extract signals of author gender from user names to select reviews where the author’s likely gender can be inferred. Using reviews authored by these gender-signaling authors, we train a deep learning classifier to quantify the gendered writing style (i.e., gendered performance) of reviews written by authors who do not send clear gender signals via their user name. We contrast the effects of gender signaling and performance on the review helpfulness ratings using matching experiments. This is aimed at understanding if an advantage is to be gained by (not) signaling one’s gender when posting reviews. While we find no general trend that gendered signals or performances influence overall review success, we find strong context-specific effects. For example, reviews in product categories such as Electronics or Computers are perceived as less helpful when authors signal that they are likely woman, but are received as more helpful in categories such as Beauty or Clothing. In addition to these interesting findings, we believe this general chain of tools could be deployed across various social media platforms.

Effective Prediction of Online Reviews for Improvement of Customer Recommendation Services by Hybrid Classification Approach

Customers post online product reviews based on their own experience. They may share their thoughts and comments on items on online shopping websites. The sentiment analysis comprises of opinion or idea process and process of sorting high rating reviews according to how well the product satisfies. Opinion mining is a technique for extracting useful data from large amounts of texts in order to use those to enhance or expand a company's operations. According to consumer evaluations, many of the goods aren't as good as they seem. It's common that buyers submit their thoughts on a product but then forget to rate it. The prior data preprocessing is more efficient to extract the features by CNN approach. This proposed methodology breaks down each user's rating prediction model into two parts: one based on the review text and other based on the user rating matrix with the help of CNN feature engineering. The goal of this study is to classify all reviews into ratings by SVM model. This proposed classification model provides good accuracy to predict the online reviews efficiently. For reviews without ratings, a further prediction of feelings is generated using multiple classifiers. The benefits of this proposed model are honed using helpfulness ratings from a small number of evaluations such as accuracy, F1 score, sensitivity, and precision. According to studies using the standard benchmark dataset, the accuracy of customized recommendation services, user happiness, and corporate trust may all be enhanced by including review helpfulness information in the recommender system.

Data Analysis and Sentiment Analysis on Amazon Reviews

In the present scenario, a person wants ease in their lives, so E-commerce has become a great and admirable involvement in providing the availability of any product at the doorsteps. But how a person can know the efficiency and originality of the product just by looking at the pictures and the details of the product on the websites. To overcome these issues the E-commerce websites have introduced the concept of the Reviews. Reviews are written by the customers who have already purchased it. Studies show that Product reviews are one of the most important points one considers during the purchasing from E-commerce websites like Flipkart, Snapdeal, Amazon and so on. This paper proposes a model that detects whether the given review is positive, negative, or neutral using the method of sentiment analysis. And using Data Analysis we can find the extension of this paper, we are planning to use a type of sentiment analysis, Opinion Mining which is the research field that predominantly makes automatic systems that will find opinion from the text written in human language. Using opinion mining, we can find whether the given reviews are fake or not. In this paper we have used Amazon food reviews data and based on the rating given by the user we are classifying reviews as positive, negative, or neutral. For positive review ratings given were 4 and 5. For negative review ratings given were 1 and 2. For neutral, rating given was 3. Based on these ratings, we are performing sentiment analysis using Scikit Learn and finding the accuracies of various classification algorithms. We are using Jupyter Notebook for visualization of documents and live coding. Keywords: Data analysis, classification algorithms, data visualization, machine learning

Evaluating Annotated Dataset of Customer Reviews for Aspect Based Sentiment Analysis

Sentiment analysis of product reviews on e-commerce platforms aids in determining the preferences of customers. Aspect-based sentiment analysis (ABSA) assists in identifying the contributing aspects and their corresponding polarity, thereby allowing for a more detailed analysis of the customer’s inclination toward product aspects. This analysis helps in the transition from the traditional rating-based recommendation process to an improved aspect-based process. To automate ABSA, a labelled dataset is required to train a supervised machine learning model. As the availability of such dataset is limited due to the involvement of human efforts, an annotated dataset has been provided here for performing ABSA on customer reviews of mobile phones. The dataset comprising of product reviews of Apple-iPhone11 has been manually annotated with predefined aspect categories and aspect sentiments. The dataset’s accuracy has been validated using state-of-the-art machine learning techniques such as Naïve Bayes, Support Vector Machine, Logistic Regression, Random Forest, K-Nearest Neighbor and Multi Layer Perceptron, a sequential model built with Keras API. The MLP model built through Keras Sequential API for classifying review text into aspect categories produced the most accurate result with 67.45 percent accuracy. K- nearest neighbor performed the worst with only 49.92 percent accuracy. The Support Vector Machine had the highest accuracy for classifying review text into aspect sentiments with an accuracy of 79.46 percent. The model built with Keras API had the lowest 76.30 percent accuracy. The contribution is beneficial as a benchmark dataset for ABSA of mobile phone reviews.

The Effect of Brand Page Commitment, Brand Awareness, Electronic Word Of Mouth and Brand Image on Purchase Intention of Xiaomi Smartphone on Social Media

Objective - Brand Xiaomi launched its first smartphone in 2011 and in 2019, Xiaomi become the 4th largest smartphone manufacturer in the world. This extraordinary achievement has brought questions about how their product is marketed and promoted and how consumers are persuaded to buy their products. This study aims to analyze factors that influence consumers purchase intentions in relation to Xiaomi smartphones. The context of the analysis in this study is social media platform, namely Xiaomi official fan page in Facebook, Twitter and Instagram in Indonesia. Methodology/Technique - A survey method is conducted and the respondents were chosen using non-probability sampling with convenience technique. Data wasanalyzed using Structure Equation Model (SEM) with smart PLS (Partial Least Square) software version. Findings - The results show that Brand Page Commitment, Brand Awareness, Electronic WOM, and Brand Image have a significant positive influence on purchase intention for Xiaomi smartphones.Brand Page Commitment of Xiaomi social media has an impact on eWOM, brand image and also consumer purchase intention. Creative content isneeded to keep customers engaged and committed to the Xiaomi social media platforms, such as photo competitions with Xiaomi cameras. Social media allows consumers to post user-generated content (such as online comments, product reviews), thus allow consumers spread the word-of mouth about the product digitally. Novelty - Social media also allowscompanies to have conversations with consumers responding to those who reach out to Xiaomi Brand page through commenting or messaging. Through their Brand Page, a company can execute social media strategies and, if done correctly, it will increase Xiaomi brand image and sales. Type of Paper - Empirical Keywords: Brand Page Commitment; Brand Awareness; Electronic WOM; Brand Image; Purchase Intention JEL Classification: M30, M39.