Medium
medium.com › @robdelacruz › sentiment-analysis-using-natural-language-processing-nlp-3c12b77a73ec
Sentiment Analysis Using Natural Language Processing (NLP) | by Robert De La Cruz | Medium
December 18, 2023 - Here are the probabilities projected on a horizontal bar chart for each of our test cases. Notice that the positive and negative test cases have a high or low probability, respectively. The neutral test case is in the middle of the probability distribution, so we can use the probabilities to define a tolerance interval to classify neutral sentiments. ... The model could improve a bit, and I may write a tuning guide as a follow-up. Sentiment analysis is a technique used in NLP to identify sentiments in text data.
Preprints.org
preprints.org › manuscript › 202410.2338
Natural Language Processing (NLP) for Sentiment Analysis: A Comparative Study of Machine Learning Algorithms[v1] | Preprints.org
October 30, 2024 - This study conducts a comparative analysis of various machine learning algorithms employed in sentiment analysis, including traditional models such as Naïve Bayes, Support Vector Machines (SVM), and Decision Trees, as well as contemporary techniques such as Random Forest, Gradient Boosting, ...
[D] Best Sentiment Analysis Model for Reddit
The best model doesn’t need the /s tag. And can process emojis. I would ❤️ that. Seriously though, I recall using some 5-class tuned model that worked really well for my use case. More on reddit.com
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April 10, 2025Any real-life sentiment analysis applications?
The value for automated sentiment analysis is when there's enough data to get interesting breakdowns instead of just a big average. Like, the average sentiment for tweets mentioning your service doesn't give you anything you don't know; however, if you: see in real-time monitoring that suddenly as of 13 minutes ago the metric drops to the floor; see that out of the 123 markets you operate in, a couple are much better/much worse than the average - something you wouldn't notice from outside of those markets; see that there's a big difference in the sentiment coming from male or female users; can track sentiment separately for specific attributes of your products and see that for product A everyone hates an aspect that's fine for other products those things can become actionable info. More on reddit.com
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November 7, 2025[P] What are some sentiment analysis tools other than TextBlob that are easy to use?
annotate your dictionary - when I did this I added words like 'fud' 'centralized' 'pump' 'dump' 'scam' etc to negative list and 'moon' 'lambo' etc to the positive list scale each tweets sentiment by retweets or views. Joe schmo's tweet shouldn't carry the same weight as Big Influence Bob. the bitcoin market was/is heavily manipulated . doesn't matter what average investor thinks when the price movement comes from big money. You need much more data on price/technicals, especially regarding dates that futures contracts expire More on reddit.com
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April 10, 2019[D] What is the best way for sentiment analysis in 2020?
Fine-tuning BERT is the way to go. You can check benchmark scores here: https://github.com/sebastianruder/NLP-progress
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August 18, 202030:33
Natural Language Processing (NLP) to analyze Customer Reviews | ...
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Advanced Sentiment Analysis with NLP Transformers + Vector Search ...
Python Sentiment Analysis Project with NLTK and ...
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End to End NLP Project | Sentiment Analysis on Amazon Reviews | ...
01:24:52
Lab 25 - Natural Language Processing and Sentiment Analysis in ...
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NLP Sentiment Analysis in Python - YouTube
Softweb Solutions
softwebsolutions.com › resources › nlp-for-sentiment-analysis
NLP for Sentiment Analysis: How It Can Impact in Businesses
April 10, 2025 - In application, sentiment analysis relies on algorithms such as Naive Bayes, Support Vector Machines (SVM), and Recurrent Neural Networks (RNN). They assist in determining the overall sentiment and emotional tone expressed in the text.
Sapien
sapien.io › home › blog › sentiment analysis in nlp: key concepts and techniques
Sentiment Analysis in NLP: Key Techniques and Insights
July 1, 2025 - LLMs and NLP techniques enable machines to: Tokenize text by breaking it into smaller units, such as words or phrases. Parse sentences to understand grammatical structures. Perform semantic analysis to interpret meaning and context. Machine learning allows sentiment analysis models to improve with experience. Machine learning ensures models evolve, adapting to new data and improving performance. Supervised learning algorithms, in particular, are widely used for training sentiment classifiers.
Analytics Vidhya
analyticsvidhya.com › home › guide to sentiment analysis using natural language processing
Guide to Sentiment Analysis using Natural Language Processing
May 2, 2025 - This analysis is powered by various algorithms such as Naive Bayes, Support Vector Machines (SVM), and Recurrent Neural Networks (RNN), which help in understanding the overall sentiment and emotional tone conveyed in the text, making it an ...
XenonStack
xenonstack.com › blog › nlp-for-sentiment-analysis
NLP for Sentiment Analysis in Customer Feedback
August 23, 2024 - These methods include text cleaning and normalization, stopword removal, negation handling, and text representation utilizing numerical features like word embeddings, TF-IDF, or bag-of-words.
Elastic
elastic.co › what-is › sentiment-analysis
What is sentiment analysis? A comprehensive technical guide | Elastic
Sentiment analysis builds on core NLP components such as tokenization, parsing, and vector representations of language. It’s often powered by pretrained transformer models (such as BERT or RoBERTa) that have been fine-tuned on datasets labeled ...
ResearchGate
researchgate.net › publication › 385389815_Natural_Language_Processing_NLP_for_Sentiment_Analysis_A_Comparative_Study_of_Machine_Learning_Algorithms
(PDF) Natural Language Processing (NLP) for Sentiment Analysis: A Comparative Study of Machine Learning Algorithms
October 29, 2024 - This study conducts a comparative analysis of various machine learning algorithms employed in sentiment analysis, including traditional models such as Naïve Bayes, Support Vector Machines (SVM), and Decision Trees, as well as contemporary ...
ScienceDirect
sciencedirect.com › science › article › pii › S2949719124000074
Recent advancements and challenges of NLP-based sentiment analysis: A state-of-the-art review - ScienceDirect
February 29, 2024 - This approach employs a genetic algorithm for optimization, incorporating linguistic knowledge from WordNet, resulting in an 8% accuracy improvement compared to existing methods, achieving an 80% accuracy rate (Bhatia et al., 2022). Furthermore, addressing text ambiguity and exploring sentiment analysis across modalities like text and images are essential. Tailoring models to specific domains and leveraging deep learning and NLP can advance sentiment analysis.
Journal-isi
journal-isi.org › index.php › isi › article › view › 1222
Impact of NLP Algorithms on Sentiment Analysis Efficiency and Accuracy | Journal of Information Systems and Informatics
September 30, 2025 - This research aims to evaluate the impact of implementing transformer-based Natural Language Processing (NLP) algorithms—such as BERT, RoBERTa, and ELECTRA—on the quality and efficiency of sentiment analysis, especially in multilingual and real-time data contexts.
GitHub
github.com › bmcclannahan › NLP-Sentiment
GitHub - bmcclannahan/NLP-Sentiment: Sentiment Analysis using Natural Language Processing · GitHub
It consists of Recurrent Neural Network (RNN) based nodes with learnable parameters. First, each word is vectorized using a dictionary vector, followed by passing through the 100-D per word embedding layer.
Starred by 23 users
Forked by 14 users
Languages Jupyter Notebook
Emeritus
emeritus.org › home › blog › ai and ml › nlp sentiment analysis: how it aids new-age opinion mining
NLP Sentiment Analysis: How it Aids New-Age Opinion Mining - Emeritus Online Courses
April 30, 2024 - ALSO READ: Top 20 Natural Language Learning Projects for Beginners to Pros · Firstly, the backbone of effective NLP sentiment analysis lies in the use of advanced machine learning algorithms. Furthermore, techniques such as natural language processing, deep learning, and neural networks are instrumental in accurately classifying sentiments from textual data.
iProyal
iproyal.com › blog › nlp-sentiment-analysis
NLP Sentiment Analysis: Techniques, Models & Use Cases
June 4, 2026 - Natural language processing (NLP) automates this by using machine learning techniques to evaluate the data and gauge its underlying emotional tone. Companies currently process millions of reviews and social media posts daily, demanding robust ...
Ayadata
ayadata.ai › home › blog › natural language processing › what is the role of opinion mining/sentiment analysis in nlp?
What Is the Role of Opinion Mining/Sentiment Analysis in NLP? - Aya Data
March 28, 2025 - Naive Bayes is a simple algorithm that calculates the probability of classes in the input text and selects the class with the highest predictive probability. In addition to supervised models, NLP is assisted by unsupervised techniques that help ...
Convin
convin.ai › en-us › blog › nlp-sentiment-analysis-insights
Unlocking NLP Sentiment Analysis: Its Impact and Insights
December 11, 2025 - Some popular sentiment analysis tools include TextBlob, VADER, IBM Watson NLU, and Google Cloud Natural Language. These tools simplify the sentiment analysis process for businesses and researchers.
LinkedIn
linkedin.com › all › engineering › artificial intelligence (ai)
What NLP models are most effective for sentiment analysis?
November 14, 2023 - Transformer models excel with large labeled datasets and ample computational resources, leveraging NLP advancements for accuracy. Pre-trained transformers, like BERT or GPT, can achieve good performance on smaller datasets through fine-tuning …see more ... As with all machine learning, the right data representation is crucial to the success of the sentiment analysis project. Thus, finding the best transformation from text to numeric data is an important step. Some representations might be better for some algorithms and domains than others and thus experiments should be carried out.
Medium
medium.com › @vishwanathmuthuraman_92476 › open-source-nlp-models-for-sentiment-analyis-6e42665ee55a
Open Source NLP Models for Sentiment Analyis | by Vishwanath muthuraman | Medium
December 26, 2022 - Gensim is an open source NLP library for Python that is focused on topic modeling and document similarity. It includes a number of algorithms for analyzing text, including Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA).
GeeksforGeeks
geeksforgeeks.org › machine learning › what-is-sentiment-analysis
What is Sentiment Analysis? - GeeksforGeeks
Sentiment Analysis is an NLP technique used to identify emotions, opinions, and attitudes expressed in textual data. It helps classify text as positive, negative, or neutral for better understanding of customer feedback and public opinion.
Published June 4, 2026
SHRM
shrm.org › topics & tools › workplace news & trends › using natural language processing for sentiment analysis
Using Natural Language Processing for Sentiment Analysis
April 9, 2024 - But what exactly is NLP? How does it work in HR? What are its pros, cons and best practices? “Natural language processing and machine learning techniques help organizations determine whether employees express positive, negative or neutral opinions in their survey answers,” Norwood said. “It also helps companies effectively determine common themes emerging from employees. Specifically, sentiment analysis allows companies to get deeper insights into survey responses, allowing them to turn their insights into actions.”
A3Logics
a3logics.com › home › enter the fascinating field of sentiment analysis in nlp
Sentiment Analysis in NLP: Types, Use Cases, and Applications
April 6, 2026 - This Sentiment Analysis in NLP uses machine learning algorithms to comprehend the text’s fundamental goal in addition to determining the text’s tone (positive, negative, or neutral).