A Context-Dependent Sentiment Analysis of Online Product Reviews based on Dependency Relationships

Consumers often view online consumer product review as a main channel for obtaining product quality information. Existing studies on product review sentiment analysis usually focus on identifying sentiments of individual reviews as a whole, which may not be effective and helpful for consumers when purchase decisions depend on specific features of products. This study proposes a new feature-level sentiment analysis approach for online product reviews. The proposed method uses an extended PageRank algorithm to extract product features and construct expandable context-dependent sentiment lexicons. Moreover, consumers’ sentiment inclinations toward product features expressed in each review can be derived based on term dependency relationships. The empirical evaluation using consumer reviews of two different products shows a higher level of effectiveness of the proposed method for sentiment analysis in comparison to two existing methods. This study provides new research and practical insights on the analysis of online consumer product reviews

A HYBRID DEEP LEARNING APPROACH FOR SENTIMENT ANALYSIS IN PRODUCT REVIEWS

Product reviews play a crucial role in providing valuable insights to consumers and producers. Analyzing the vast amount of data generated around a product, such as posts, comments, and views, can be challenging for business intelligence purposes. Sentiment analysis of this content helps both consumers and producers gain a better understanding of the market status, enabling them to make informed decisions. In this study, we propose a novel hybrid approach based on deep neural networks (DNNs) for sentiment analysis in product reviews, focusing on the classification of sentiments expressed. Our approach utilizes the recursive neural network (RNN) algorithm for sentiment classification. To address the imbalanced distribution of positive and negative samples in social network data, we employ a resampling technique that balances the dataset by increasing samples from the minority class and decreasing samples from the majority class. We evaluate our approach using Amazon data, comprising four product categories: clothing, cars, luxury goods, and household appliances. Experimental results demonstrate that our proposed approach performs well in sentiment analysis for product reviews, particularly in the context of digital marketing. Furthermore, the attention-based RNN algorithm outperforms the baseline RNN by approximately 5%. Notably, the study reveals consumer sentiment variations across different products, particularly in relation to appearance and price aspects

Learning domain-specific sentiment lexicons with applications to recommender systems

Search is now going beyond looking for factual information, and people wish to search for the opinions of others to help them in their own decision-making. Sentiment expressions or opinion expressions are used by users to express their opinion and embody important pieces of information, particularly in online commerce. The main problem that the present dissertation addresses is how to model text to find meaningful words that express a sentiment. In this context, I investigate the viability of automatically generating a sentiment lexicon for opinion retrieval and sentiment classification applications. For this research objective we propose to capture sentiment words that are derived from online users’ reviews. In this approach, we tackle a major challenge in sentiment analysis which is the detection of words that express subjective preference and domain-specific sentiment words such as jargon. To this aim we present a fully generative method that automatically learns a domain-specific lexicon and is fully independent of external sources. Sentiment lexicons can be applied in a broad set of applications, however popular recommendation algorithms have somehow been disconnected from sentiment analysis. Therefore, we present a study that explores the viability of applying sentiment analysis techniques to infer ratings in a recommendation algorithm. Furthermore, entities’ reputation is intrinsically associated with sentiment words that have a positive or negative relation with those entities. Hence, is provided a study that observes the viability of using a domain-specific lexicon to compute entities reputation. Finally, a recommendation system algorithm is improved with the use of sentiment-based ratings and entities reputation

Enhancing Lexical Sentiment Analysis using LASSO Style Regularization

In the current information age where expressing one’s opinions online requires but a few button presses, there is great interest in analyzing and predicting such emotional expression. Sentiment analysis is described as the study of how to quantify and predict such emotional expression by applying various analytical methods. This realm of study can broadly be separated into two domains: those which quantify sentiment using sets of features determined by humans, and approaches that utilize machine learning. An issue with the later approaches being that the features which describe sentiment within text are challenging to interpret. By combining VADER which is short for Valence Aware Dictionary for sEntiment Reasoning; a lexicon model with machine learning tools (simulated annealing) and k-fold cross validation we can improve the performance of VADER within and across context. To validate this modified VADER algorithm we contribute to the literature of sentiment analysis by sharing a dataset sourced from Steam; an online video game platform. The benefits of using Steam for training purposes is that it contains several unique properties from both social media and online web retailers such as Amazon. The results obtained from applying this modified VADER algorithm indicate that parameters need to be re-trained for each dataset/context. Furthermore that using statistical learning tools to estimate these parameters improves the performance of VADER within and across context. As an addendum we provide a general overview of the current state of sentiment analysis and apply BERT a Transformer-based neural network model to the collected Steam dataset. These results were then compared to both base VADER and modified VADER

Sentiment Analysis Using Deep Learning: A Comparison Between Chinese And English

With the increasing popularity of opinion-rich resources, opinion mining and sentiment analysis has received increasing attention. Sentiment analysis is one of the most effective ways to find the opinion of authors. By mining what people think, sentiment analysis can provide the basis for decision making. Most of the objects of analysis are text data, such as Facebook status and movie reviews. Despite many sentiment classification models having good performance on English corpora, they are not good at Chinese or other languages. Traditional sentiment approaches impose many restrictions on the raw data, and they don't have enough capacity to deal with long-distance sequential dependencies. So, we propose a model based on recurrent neural network model using a context vector space model. Chinese information entropy is typically higher than English, we therefore hypothesise that context vector space model can be used to improve the accuracy of sentiment analysis. Our algorithm represents each complex input by a dense vector trained to translate sequence data to another sequence, like the translation of English and French. Then we build a recurrent neural network with the Long-Short-Term Memory model to deal the long-distance dependencies in input data, such as movie review. The results show that our approach has promise but still has a lot of room for improvement

Emotion Detection and Classification using Hybrid Feature Selection and Deep Learning Techniques

Image sentiment analysis has gained significant attention due to the increasing availability of user-generated content on various platforms such as social media, e-commerce websites, and online reviews. The core of our approach lies in the deep learning model, which combines the strengths of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. The CNN component captures local dependencies and learns high-level features, while the LSTM component captures long-term dependencies and maintains contextual information. By fusing these two components, our model effectively captures both local and global context, leading to improved sentiment analysis performance. During the execution first select the context and generate visual feature vector for generation of captions. The EfficientNetB7 model is applied in order to construct the image description for every individual picture. The Attention-based LSTM as well as Gated Recurrent Unit (GRU) greedy method are the two approaches that are utilized in the process of classifying sentiment labels. The proposed research has been categorized into three different phases. In Phase 1 describe various data preprocessing and normalization techniques. It also demonstrates training using RESNET-101 deep learning-based CNN classification algorithm. In Phase 2 extract the various features from the selected context of input image. The context has been selected based on detected objects from the image and generates a visual caption for the entire dataset. The      generated captions are dynamically used for model training as well as testing to both datasets. The EfficientNet module has used for generation of visual context from selected contexts. Finally in phase 3 classification model has built using a Deep Convolutional Neural Network (DCNN). The proposed algorithm classified the entire train and test dataset with different cross- validations such as 5-fold, 10-fold and 15-fold etc. The numerous activation functions are also used for evaluation of the proposed algorithm in different ways. The higher accuracy of the proposed model is 96.20% sigmoid function for 15-fold cross validation

Aspect Based Sentiment Analysis for Large Documents with Applications to US Presidential Elections 2016

Aspect based sentiment analysis (ABSA) deals with the fine grained analysis of text to extract entities and aspects and analyze sentiments expressed towards them. Previous work in this area has mostly focused on data of short reviews for products, restaurants and services. We explore ABSA for human entities in the context of large documents like news articles. We create the first-of-its-kind corpus containing multiple entities and aspects from US news articles consisting of approximately 1000 annotated sentences in 300 articles. We develop a novel algorithm to mine entity-aspect pairs from large documents and perform sentiment analysis on them. We demonstrate the application of our algorithm to social and political factors by analyzing the campaign for US presidential elections of 2016. We analyze the frequency and intensity of newspaper coverage in a cross-sectional data from various newspapers and find interesting evidence of catering to a partisan audience and consumer preferences by focusing on selective aspects of presidential candidates in different demographics

Sentiment Analysis Regarding Candidate Presidential 2024 Using Support Vector Machine Backpropagation Based

This research has the potential to make an important contribution to the development of computationally-based sentiment analysis, particularly in the political context. Anies Baswedan, Ganjar Pranowo, and Prabowo Subianto, three candidates for the presidency of Indonesia, are examined using a Backpropagation-based Support Vector Machine (SVM) methodology in this study. This approach is used to categorize emotions into three groups: neutral, adverse, and favorable. Between July 1 and July 30, 2023, data on tweets mentioning the three presidential contenders was gathered. After processing the data, SVM was used while lowering the backpropagation process. The study's findings demonstrate that the performance of the model in determining public sentiment is greatly enhanced by the application of backpropagation-based SVM techniques. For each presidential contender, the evaluation was conducted using the f1 score, recall, and precision metrics. The evaluation's findings indicate that while the model struggles to distinguish between favorable and negative feelings toward particular presidential contenders, it performs better when categorizing neutral feelings. The SVM model is more accurately able to identify popular sentiment toward the three presidential candidates when the backpropagation approach is used. The results of the sentiment analysis are also represented by word clouds for each presidential contender, giving an intuitive sense of the words that are frequently used in public discourse. This study sheds light on the possibilities of using Twitter data to analyze political sentiment using the backpropagation-based SVM algorithm.

Analyzing Sentiment Polarity Reduction in News Presentation through Contextual Perturbation and Large Language Models

In today's media landscape, where news outlets play a pivotal role in shaping public opinion, it is imperative to address the issue of sentiment manipulation within news text. News writers often inject their own biases and emotional language, which can distort the objectivity of reporting. This paper introduces a novel approach to tackle this problem by reducing the polarity of latent sentiments in news content. Drawing inspiration from adversarial attack-based sentence perturbation techniques and a prompt based method using ChatGPT, we employ transformation constraints to modify sentences while preserving their core semantics. Using three perturbation methods: replacement, insertion, and deletion coupled with a context-aware masked language model, we aim to maximize the desired sentiment score for targeted news aspects through a beam search algorithm. Our experiments and human evaluations demonstrate the effectiveness of these two models in achieving reduced sentiment polarity with minimal modifications while maintaining textual similarity, fluency, and grammatical correctness. Comparative analysis confirms the competitive performance of the adversarial attack based perturbation methods and prompt-based methods, offering a promising solution to foster more objective news reporting and combat emotional language bias in the media.Comment: Accepted in ICON 202