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Sentiment Analysis

Fabian Tomaschek · OSF Preprints (OSF Preprints) · 2023

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Sentiment Analysis

2022

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sentiment analysis

rohit kumar karedla · Zenodo (CERN European Organization for Nuclear Research) · 2025

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Sentiment Analysis

Huaping Zhang, Jianyun Shang · 2025

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Sentiment Analysis

Kohári, Gergely · University of Debrecen Electronic Archive (University of Debrecen) · 2011

The aim of sentiment analysis is to gain specific information from any given text that is
\ngrammatical. This extraction provides us with more accurate data about the meaning, intended by
\nthe text's creator. The key to this information is sentiment analysis. Without this analysis the
\npossible interpretation of the author’s sentence could not be detected or might be misunderstood. In
\nthe case of an ambiguous sentence the recipient can rely on such an analysis to decide what the
\noriginal meaning was. This way all the computable information can be graded, on the phrase and
\nsentence level of a text. The notion of opinions about the words and sentences can help to evaluate
\nin complex sentences. In the process of extracting information natural language processing,
\ncomputational linguistics and text analytics is applied. On the long run this model can help the users
\nto receive useful data. In the recent studies of Opinion mining and Sentiment analysis the
\nresearchers come to a logical consequence. To a certain point, mostly about seventy percent of a
\ndata can accepted as to be valid. Among the methods of Sentiment Analysis this paper will also
\nmention a few existing issues and ideas about the problems it has to overcome

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Sentiment Analysis

Fabian Tomaschek · OSF Preprints (OSF Preprints)

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Sentiment Analysis

Pushpak Bhattacharyya · 2013

Sentiment analysis is an exciting new field of research in Artificial Intelligence combining Natural Language Processing, Machine Learning and Psychology. Since 2000, due to the proliferation of huge amounts of opinions in electronic form on the web, on social networks and on blogs, automatic means of polarity (positive, negative and neutral) detection in texts flourished in leaps and bounds. Individual and organizations with public interface can no longer afford to be oblivious of sentiments expressed about them in electronic form. In the present tutorial, we will first discuss the foundations of sentiment analysis, covering knowledge based and machine learning based techniques. Feature engineering forms an important part of the task. Starting from word level features we move to more sophisticated text units and explore their efficacy for SA. After this, we describe our research on sentiment analysis that attempts to advance the state of the art by tackling new text form (Tweets), a new task (Thwarting), new language (Indian languages) and new features (word senses instead of words). Tweets are noisy texts; they need text cleaning and normalization. Exploitation of discourse relations like ‘although’, ‘still’, ‘but’ and so on improve the accuracy considerably. Languages differ in terms of annotated resources. However, projecting the parameters learnt from one language to another can ameliorate the problem of resource scarcity. We show the use of these ideas for sentiment analysis of Indian languages. We demonstrate that working with senses instead of just words is not only advantageous from the point of view of multilinguality, but also for accuracy. Finally, problems like thwarting (reversal of polarity just by a single statement at a critical place in the text) and sarcasm are hard problems. We present some of our findings in these tasks too.

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Sentiment Analysis

Raoul Biagioni · SpringerBriefs in cognitive computation · 2016

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Sentiment Analysis

Robert A. Stine · Annual Review of Statistics and Its Application · 2018

Sentiment analysis labels a body of text as expressing either a positive or negative opinion, as in summarizing the content of an online product review. In this sense, sentiment analysis can be considered the challenge of building a classifier from text. Sentiment analysis can be done by counting the words from a dictionary of emotional terms, by fitting traditional classifiers such as logistic regression to word counts, or, most recently, by employing sophisticated neural networks. These methods progressively improve classification at the cost of increased computation and reduced transparency. A common sentiment analysis task, the classification of IMDb (Internet Movie Database) movie reviews, is used to illustrate the methods on a common task that appears frequently in the literature.

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Sentiment Analysis

Josh Sanon · OSF Preprints (OSF Preprints) · 2020

Running different Machine Learning algorithms to figure out efficiency pertaining to solving Sentiment Analysis problems. In this project the algorithms attempt to classify a user's rating (1-5 stars) of a product based on the comments left by them

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sentiment analysis

rohit kumar karedla · Zenodo (CERN European Organization for Nuclear Research) · 2025

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Sentiment Analysis

C. Sindhu, G. Vadivu · 2023

With the explosion of new web technology, user generated content is all over the web and is accessible almost everywhere in the world. Such accessibility has paved way for an easier suggestion-seeking paradigm. In this fast-paced world, we humans find no time to stand and stare, which well suits this scenario, where we don’t want to read through each and every review of a product or a service. Sentiment analysis is, thus an inevitable process, which helps every manufacturer and brand-owner to analyze the pros and cons of their own items and the competing products. Furthermore, be it any service or product, the experience shared about it is multi-dimensional with loads of emotions splashed into it. This chapter can help researchers explore further into the world of sentiment analysis.

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Sentiment Analysis

Margarita Tavkazakova, Maral Togtokhbaatar · 2023

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Sentiment Analysis

Liang Feng · Mendeley Data · 2023

This is all the data that we collected from SLR during our research process.

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Sentiment analysis

Benqiao Jiang, Kevin K. Byon · Edward Elgar Publishing eBooks · 2024

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Sentiment Analysis

Hong Peng, Jun Wang · Computational intelligence methods and applications · 2024

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Sentiment Analysis

Shaik Ashraf, Satyam Abhinav, Dyaga Vinay et al. · REST Journal on Data Analytics and Artificial Intelligence · 2025

With the growing popularity of online shopping, it has become essential to have reliable systems for evaluating products. This project creates a system that uses advanced natural language processing (NLP) techniques like Sentiment Analysis, Opinion Mining, and Aspect-Based Sentiment Analysis (ABSA) to provide accurate scores for products. It uses the Distil BERT model to analyze reviews and give ratings from 1 to 5 based on what customers write. ABSA helps the system break down feedback into specific areas, such as quality, price, and durability, offering more detailed insights. The system also detects emotions like happiness, anger, or sadness in reviews, giving a clearer picture of user opinions. It supports multiple languages, making it useful for analyzing reviews globally. Built with tools like Python, Beautiful Soup, NLTK, and Transformers, this system processes online reviews and converts them into easy-to-understand scores, helping buyers make better decisions.

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Sentiment analysis.

Mahmudul Hasan (750862), Tanver Ahmed (19139219), Md. Rashedul Islam (8298888) et al. · Figshare · 2024

<div><p>The rise of social media has changed how people view connections. Machine Learning (ML)-based sentiment analysis and news categorization help understand emotions and access news. However, most studies focus on complex models requiring heavy resources and slowing inference times, making deployment difficult in resource-limited environments. In this paper, we process both structured and unstructured data, determining the polarity of text using the TextBlob scheme to determine the sentiment of news headlines. We propose a Stochastic Gradient Descent (SGD)-based Ridge classifier (RC) for blending SGDR with an advanced string processing technique to effectively classify news articles. Additionally, we explore existing supervised and unsupervised ML algorithms to gauge the effectiveness of our SGDR classifier. The scalability and generalization capability of SGD and L2 regularization techniques in RCs to handle overfitting and balance bias and variance provide the proposed SGDR with better classification capability. Experimental results highlight that our string processing pipeline significantly boosts the performance of all ML models. Notably, our ensemble SGDR classifier surpasses all state-of-the-art ML algorithms, achieving an impressive 98.12% accuracy. McNemar’s significance tests reveal that our SGDR classifier achieves a 1% significance level improvement over K-Nearest Neighbor, Decision Tree, and AdaBoost and a 5% significance level improvement over other algorithms. These findings underscore the superior proficiency of linear models in news categorization compared to tree-based and nonlinear counterparts. This study contributes valuable insights into the efficacy of the proposed methodology, elucidating its potential for news categorization and sentiment analysis.</p></div>

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Sentiment Analysis

Soniya Zore, Amol Bhosale, Pallavi Vijay Chavan · International Journal of Electronics and Computer Applications · 2024

Social media platforms like Twitter have become a rich source of real-time data and public sentiment. Analysing sentiment on Twitter is essential for various applications, from brand monitoring to political analysis. This work focuses on Twitter sentiment analysis, employing natural language processing (NLP) techniques to categorize tweets as positive, negative, or neutral. We collect a large dataset of tweets, pre-process the text, and train machine learning models to predict sentiment. Our goal is to provide insights into public sentiment on various topics, trends, and events, which can be valuable for decision-makers in diverse domains.

Cited by 1Open access

Sentiment Analysis

Daniel Traian Pele · 2024

This is the repository of the paper "BitMood: AI Analysis of Bitcoin Trends via Facebook Emotion", authors Alexandra Ioana Conda, Ștefan Găman, Raul Cristian Bâg, Miruna Mazurencu-Marinescu-Pele, Daniel Traian Pele, Wolfgang Karl Härdle. - BitMood/1. Sentiment Analysis at main · QuantLet/BitMood

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