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Sentiment Analysis of Online Hotel Reviews Employing Bidirectional GRU with Attention Mechanism
Online hotel reviews are a more reliable resource for potential hotel guests. Sentiment analysis is a branch of text mining, Natural Processing Language that seeks to identify personality traits, emotions, and other factors. Deep Learning algorithms such as LSTM and GRU have successfully generated context information in sequence learning. However, deep learning cannot focus on the words that contribute the most and cannot capture important content information. This research aims to overcome the inability of LSTM and GRU to capture information. The results are satisfactory, with 93.12% accuracy, 95% ROCAUC, and 95.28% precision recall. This research paper helps managers identify areas to improve their products and services, target marketing campaigns, and identify customer churn. 2024 IEEE. -
Sentiment analysis of sentences used in Indian local languages using Transfer Learning
The growing number of online communication channels has led to a dramatic increase in the volume of user-generated content across all media. Sentiment analysis is given more force by having access to these varying perspectives and feelings. Due to a lack of standardised labelled data, sentiment analysis becomes much more difficult. The goal of sentiment analysis is to determine, from a text sample, an individual's likely emotional reaction to a given event or point of view. When estimating the tone of a text, polarity analysis is commonly used. Negative, Positive and neutral are the labels used by sentiment classifiers. This chapter provides a framework for effective sentiment analysis of sentences from four local language datasets, including Hindi, English, Bangla, and Marathi, using transfer learning. The results show that the BERT model achieves a high level of accuracy when evaluated using random forests, nae Bayes, and decision trees. 2026 -
Sentiment Analysis of Stress Among the Students Amidst the Covid Pandemic Using Global Tweets
Covid-19 pandemic has affected the lives of people across the globe. People belonging to all the sectors of the society have faced a lot of challenges. Strict measures like lockdown and social distancing have been imposed several times by governments throughout the world. Universities had to incorporate the online method of teaching instead of the regular offline classes to implement social distancing. Online classes were beneficial to most of the students; at the same time, there were many difficulties faced by the students due to lack of facilities to attend classes online. Students faced a lot of challenges, and a sense of anxiety was prevalent during the uncertain times of the pandemic. This research article analyzes the stress among students considering the tweets across the globe related to students stress. The algorithms considered for classification of tweets as positive or negative are support vector machine (SVM), bidirectional encoder representation from transformers (BERT), and long short-term memory (LSTM). The accuracy of the abovementioned algorithms is compared. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Sentiment Analysis on Amazon Product Review
Users throughout the world may now access massive amounts of data thanks to the internet and social media platforms. [5] In every facet of human existence, electronic commerce (e-commerce) plays a crucial role. E-commerce is a marketing approach that enables businesses and consumers to buy and sell things via the internet. When buyers look for product information and compare alternatives online, they generally have access to dozens or hundreds of product reviews from alternative shoppers. Machine learning is the most appropriate approach to training a neural network in today's age of practical artificial intelligence. So implementing a model to polarize those reviews and learn from them would make passing hundreds of comments a lot easier. [24] The interpretation will be a very basic product with positive, neutral, and negative polarization. The product is checked. This study suggests a sentiment evaluation model for shopper reviews based on the object and emotive word mining for emotional level analysis using machine learning approaches. 2022 IEEE. -
Sentiment Analysis on Banking Feedback and News Data using Synonyms and Antonyms
Sentiment analysis is crucial for deciphering customers enthusiasm, frustration, and the market mood within the banking sector. This importance arises from financial datas specialized and sensitive nature, enabling a deeper understanding of customer sentiments. In todays digital and social marketing landscape within the banking and financial sector, sentiment analysis is significant in shaping customer insights, product development, brand reputation management, risk management, customer service improvement, fraud detection, market research, compliance regulations, etc. This paper introduces a novel approach to sentiment analysis in the banking sector, emphasizing integrating diverse text features to enable dynamic analysis. This proposed approach aims to assess the sentiment score of distinct words used within a document and classify them as positive, negative, or neutral. After rephrasing sentences using synonyms and antonyms of unique words, the system calculates sentence similarity using a distance control mechanism. Then, the system updates the dataset with the positive, negative, and neutral labels. Ultimately, the ELECTRA model utilizes the self-trained sentiment-scored data dictionary, and the newly created dataset is processed using the SoftMax activation function in combination with a customized ADAM optimizer. The approachs effectiveness is confirmed through the analysis of post-bank customer feedback and the phrase bank dataset, yielding accuracy scores of 92.15% and 93.47%, respectively. This study stands out due to its unique approach, which centers on evaluating customer satisfaction and market sentiment by utilizing sentiment scores of words and assessing sentence similarities. 2023, Science and Information Organization. All rights reserved. -
Sentiment Analysis On Covid-19 Related Social Distancing Across The Globe Using Twitter Data
Covid 19 pandemic has devastated the lives of several people across the globe. Social distancing is considered a major preventive measure to stop the spread of Covid 19. The practice of social distancing has caused a sense of loneliness and mental health problems in society. The aim of this study is to consider global tweet data with social distancing keywords for analyzing the sentiments behind them. Classification of tweets as positive or negative is carried out using Support Vector Machine and Logistic Regression. The Electrochemical Society -
Sentiment Analysis on Educational Tweets: A Case of National Education Policy 2020
Due to COVID-19 pandemic lockdowns, the transition from traditional class-room-based approaches, there has been rise in online education. There is a growing need to adopt the best global academic and innovative practices and implement the National Education Policy-2020 (NEP) in Indian education. This study uses a dataset, NEPEduset, created by gathering tweets about education. An attempt has been made in this study to examine the tweets by preprocessing, generating labels or sentiments using standard tools and libraries in Python language, applying and comparing various machine learning (ML) algorithms. ML approaches are powerful and used in various applications ranging from sentiment analysis, text analysis, natural language processing (NLP), image processing, object detection. ML methods are widely used in sentiment analysis tasks and text annotations. This work uses Text-Blob, Valence Aware Dictionary for Sentiment Reasoning (VADER), and a Customized method, SentiNEP to analyze the sentiment score of tweets' text. SentiNEP method is shown is produce better results for various experiments conducted for the dataset, NEPEduset. Various supervised ML models have been applied for text classification of user sentiment. Word2Vec feature extraction technique has been applied to build and evaluate the models. Performance metrics such as precision, accuracy, F1 score and recall have been used to evaluate the ML models. The results reveal that the support vector machine and random forest classifiers achieve higher accuracy with Word2Vec. The performance results have been compared with VADER, TextBlob and SentiNEP. It has been found that the SentiNEP method produces better results. 2023 IEEE. -
Sentiment Analysis on Indian Government Schemes Using Twitter data
People use social media for entertainment, fetching information, news, business, communication and many more. Few of such social media applications are Facebook, Twitter, WhatsApp, Snapchat and so on. Twitter is one among the micro blogging websites. We are using Twitter mainly because it has gained a lot of media attention. The text written is referred to as tweets, where a common man can tweet or can write their hearts out. We would be fetching the direct responses from the public and hence the data is more real-time. First step is to fetch the tweets on a particular scheme using python language code followed by the cleaning process then comes the creation of bag of words. Later these bags of words are given as an input to the algorithms. Finally, after training the algorithms, we will be getting the sentiment of the public on that scheme. 2019 IEEE. -
Sentiment Analysis on Live Webscraped YouTube Comments Using VADER Sentiment Analyzer
After the covid disease came in the beginning of 2020s, the amount of people using social medias has increased dramatically. So as an effect of that, the viewers and engagement in one of the worlds largest platform by google called YouTube also increased. So many new content creators also born during these times. So this project is getting the sentiment from the audience or user to the content creators by which they can improve their content quality. This research holds promise in harnessing the power of sentiment analysis to enhance the overall YouTube experience and inform content creators and platform administrators in their decision-making processes. Understanding these trends is vital for content creators, as it can offer invaluable insights into viewer engagement and preferences. By gaining a deeper understanding of how viewers react to content, creators can refine their strategies, tailor their content to their audience, and enhance the overall quality of videos. By incorporating sentiment information into recommendations, the platform can suggest videos that resonate more effectively with users, thereby increasing engagement and satisfaction. The identification of negative sentiment and harmful comments enables YouTubes content moderation systems to proactively address issues such as hate speech, harassment, and toxicity. This, in turn, contributes to a safer and more welcoming space for users to share their thoughts and opinions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Sentiment analysis on social media data using intelligent techniques
Social media gives a simple method of communication technology for people to share their opinion, attraction and feeling. The aim of the paper is to extract various sentiment behaviour and will be used to make a strategic decision and also aids to categorize sentiment and affections of people as clear, contradictory or neutral. The data was preprocessed with the help of noise removal for removing the noise. The research work applied various techniques. After the noise removal, the popular classification methods were applied to extract the sentiment. The data were classified with the help of Multi-layer Perceptron (MLP), Convolutional Neural Networks (CNN). These two classification results were checked against the others classified such as Support Vector Machine (SVM), Random Forest, Decision tree, Nae Bayes, etc., based on the sentiment classification from twitter data and consumer affairs website. The proposed work found that Multi-layer Perceptron and Convolutional Neural Networks performs better than another Machine Learning Classifier. International Research Publication House. -
Sentiment Analysis on Time-Series Data Using Weight Priority Method on Deep Learning
Sentiment Analysis (SA)is the process to gain an overview of the public opinion on certain topics and it is useful in commerce and social media. The preference on certain topics can be varied on different time periods. To analyze the sentiments on topics in different time periods, priority weight based deep learning approaches like Convolutional-Long Short-Term Memory (C-LSTM)and Stacked- Long Short-Term Memory (S-LSTM)is explored and analyzed in this research. The research method focuses on three phases. In the first phase text data (review given by the customers on various products)is collected from social networking e-commerce site and temporal ordering is done. In the second phase, different deep learning models are created and trained with different time-series data. In the final phase the weights are assigned based on temporal aspect of the data collected. For the obtained results verification and validation processes are carried out. Precision and recall measures are computed. Results obtained shows better performance in terms of classification accuracy and F1-score. 2019 IEEE. -
Sentiment analysis with NLP: A catalyst for sales in analyzing the impact of social media ads and psychological factors online
This chapter explores the role of sentiment analysis, powered by NLP, in boosting sales amidst "Intersection of AI and Business Intelligence in Data-Driven DecisionMaking." It analyzes how social media ads and psychological factors shape online shopping behavior, demonstrating how sentiment analysis drives digital commerce sales. Sentiments from platforms like Twitter, Facebook, and Instagram are categorized into positive, negative, or neutral using advanced NLP algorithms. The chapter delves into psychological factors such as trust, credibility, brand perception, and emotional responses triggered by social media ads. Through sentiment analysis, patterns and correlations between sentiment expressions and consumer actions are revealed, illuminating the impact of social media advertising on online shopping behavior. This insight aids marketers in optimizing digital strategies, developing effective campaigns to enhance sales performance, and engaging customers in the online shopping domain. 2024, IGI Global. All rights reserved. -
Sentiment and Emotion Analysis of Significant Diseases in India and Russia
Healthcare organizations need this information to understand and treat the patient's concerns. The motivation for this kind of analysis is how patients provide this information while wrapping it in their thoughts and emotions. It is less practicable to manually study all the free and abundant health-related knowledge accessible online to arrive at decisions that might contribute to an immediate and beneficial decision. Sentiment analysis methods perform this function through automated procedures with minimal human intervention. In this paper, an investigation is conducted to compare the region-wise, language-wise, and sentiment analysis of the tweets collected from Russia and India. The results obtained through research have shown some significant characteristics of the language models used for language detection. The inferenc and analysis obtained from the observations are included in this paper. 2023 IEEE. -
Sentiment and emotion analysis using machine learning techniques
Sentiment analysis and text emotion identification have grown in prominence due to their wide range of applications in fields such as psychology, artificial intelligence, human-computer interaction, and so on. There are numerous Machine Learning approaches available for emotion recognition and sentiment analysis. The chapter also delves into the key procedures of data collecting, preprocessing, and emphasising the necessity of good data in training effective models. Real-world applications from a variety of disciplines, including business, healthcare, and entertainment, are investigated to demonstrate the practical utility of these strategies. The chapter also covers the issues of ambiguity, context-awareness, and cross-linguistic disparities, as well as providing suggestions for future study paths. This chapter provides a detailed exploration of machine learning approaches to sentiment and emotion analysis, making it a valuable resource for researchers, practitioners, and students interested in using machine learning to understand and interpret emotional content in textual data. 2025, IGI Global Scientific Publishing. All rights reserved. -
Sentimental analysis on Amazon book reviews: A deep learning approach
[No abstract available] -
Sentimental Analysis on Online Education Using Machine Learning Models
Sentimental analysis is a simple natural language processing technique for classifying and identifying the sentiments and views represented in a source text. Corona pandemic has shifted the focus of education from traditional classrooms to online classes. Students mental and psychological states alter as a result of this transition. Sentimental study of the opinions of online education students can aid in understanding the students learning conditions. During the corona pandemic, only, students enrolled in online classes were surveyed. Only, students who are in college for pre-graduation, graduation, or post-graduation were used in this study. To grasp the pupils feelings, machine learning models were developed. Using the dataset, we were able to identify and visualize the students feelings. Students favorable, negative, and neutral opinions can be successfully classified using machine learning algorithms. The Naive Bayes method is the most accurate method identified. Logistic regression, support vector machine, decision tree, and random forest these algorithms also gave comparatively good accuracy. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Sentimental analysis on voice using AWS comprehend
Sentimental analysis plays an important role in these days because many start-ups have started with user-driven content [1]. Sentiment analysis is an important research area in natural language processing. Natural language processing has a wide range of applications like voice recognition, machine translation, product review, aspect-oriented product analysis, sentiment analysis and text classification etc [2]. This process will improve the business by analyse the emotions of the conversation. In this project author going to perform sentimental analysis using Amazon Comprehend. Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to extract the content of the document. By using this service can extract the unstructured data like images, voice etc. Thus, will identify the emotions of the conversation and give the output whether the conversation is Positive, Negative, Neutral, or Mixed. To perform this author going to use some services from Aws like s3 which is used for the data store, Transcribe which is used for converting the audio to text, Aws Glue is used to generate the metadata from the comprehend file, Aws Comprehend is used to generate the sentiment file from the audio, Lambda is used to trigger from the data store s3, Aws Athena is used to convert text into structured data and finally there is quick sight where he can visualize the data from the given file. 2020 IEEE. -
SentimentViz: Leveraging RoBERTa in Python for Advanced Sentiment Analysis and Decision-Making for a Famous Indian FMCG (Ayurvedic) Brand
Indian consumer preferences for Ayurvedic brands increasingly turn to the marketplace for well-being. Ayurveda has a deep-rooted history in emerging economies like India, and its increasing role in health, wellness, and exports contributes to Indias economic development. The consumption and changing lifestyle patterns significantly contribute to achieving the United Nations Sustainable Development Goals (SDGs). The primary objective of this study is to explore consumer sentiment that includes perceptions, feelings, and attitudes toward these natural healthcare products contributing to specific SDG targets, leading to good health and well-being. In a data-driven world where governments and businesses seek insights from vast amounts of unstructured text data, sentiment analysis plays a pivotal role in decision-making. Sentiment analysis helps analyze different aspects of unstructured data, including customer experience and insights generated in terms of usage, challenges, and preferences and ultimately helps manage customer engagement. The sentiment analysis requires understanding the context, grouping similar words, removing unrelated content, and then gauging the sentiment of the text. There has always been a challenge to contextualize and gauze the deeper sentiments and create scalable solutions. To build on this need for deeper sentiment understanding and scalable solutions, SentimentViz is a proposed accelerator as part of this paper that leverages Python and chooses the best methodology for text-mining problems. It enables real-time analysis with robust visualization capabilities: In this study, the SentimentViz accelerator is leveraged to estimate the sentiment of 9 products using a robust data science framework and best-of-the-class ML techniques. The detailed consumer sentiment analysis helped to develop a deeper understanding of the value of FMCG (Ayurvedic products) for an emerging economy like India. This will help marketers build targeted marketing campaigns, brand health monitoring, and customer retention strategies through informed decisions. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Separate Electorate
In colonial India, the category of Depressed Classes was approached through two distinct lenses as a socio-political issue and a moral and cultural issue. Two towering figures represented these perspectives B. R. Ambedkar and M. K. Gandhi. While Gandhi earnestly sought to address untouchability and other socio-cultural challenges faced by Depressed Classes through moral teachings, his approach was contested for being quite ineffective. Ambedkar, on the contrary, viewed the problem as deeply entrenched within religion and culture, advocating for socio-political empowerment as the solution. This chapter maps the historical events leading to Ambedkars successfully lobbying of British India for a separate electorate for Depressed Classes. It examines Gandhis vehement objection to the same based on his understanding that such provision would be detrimental to Depressed Classes, Hinduism, and national unity. Consequently, this chapter analyses how Ambedkar was forced to come to an agreement with Gandhi and sign the Poona Pact that put an end to proposed separate electorate but granted additional reserved seats to Depressed Classes. This chapter provides historical contexts and discusses the debates around the political representation of the minorities to address the larger question of political share of Depressed Classes in Indian electorate. 2026 selection and editorial matter, Mahitosh Mandal and Sanjiv Kondekar; individual chapters, the contributors. -
Separated/Divorced Individuals Experiences with the Legal System in India: A Qualitative Inquiry
This study identifies systemic flaws and biases in the Indian judicial system, highlighting areas for reform. Through purposive sampling, 15 separated/divorced participants (nine male, six female) were analyzed via semi-structured interviews and Interpretative Phenomenological Analysis (IPA) using Atlas.ti 23 software. Analysis derived one group experiential theme: Judicial Process and Law Enforcement, with four sub-themes: The Contradictory Positions of the Court, Nonchalance toward False Accusations, Encounter with Legal Professionals, and Two Sides of Law Enforcement. Participants revealed conflicting experiences, with few achieving justice while others faced substantial delays. Male participants often encountered allegations without evidence. Regardless of gender, feelings of distress and helplessness were prevalent due to court procedures. The findings highlight the urgent need for reforms like procedural transparency to mitigate the trauma of divorce in India and emphasize a gap in existing literature on judicial effects in separation cases. Recommendations for future research are suggested. 2025 Taylor & Francis Group, LLC.