A sentiment annotated dataset in Swedish containing emojis.
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Updated
Jul 8, 2023
A sentiment annotated dataset in Swedish containing emojis.
Aspect-based sentiment analysis (ABSA) algorithm to identify product review categories and corresponding sentiment for each category.
Sentiment Analysis using different feature extraction techniques
Sentiment Analysis on changing attitudes of Reddit users towards Chat GPT
How to Scrape Twitter Data for Sentiment Analysis with Python and Power BI
Movie ratings prediction
Analyzing 9801 tweets using Python's Tweepy library and VADER model for sentiment analysis. Results mapped to emotions ('happy,' 'curious,' 'neutral,' 'upset,' 'angry'). Insights gathered on popular discussions, famous accounts, hashtags, and tweet locations for a deeper understanding of user sentiments.
SenTrack App - Roberta Sentiment Analysis.
This project was run in DataBricks using spark to analyze the recent news in 'cancer' for sentiment evaluation. The goal of this project is to practice traditional NLP like tokenization, stopwords, CV and TF-IDF, N-grams. Also, this project applied tools like AWS S3, athena, QuickSight etc. to address big data.
Implementation of Support Vector Machine Algorithm in Sentiment Analysis using IndoNLU Dataset
A prediction of the Nigeria presidential election winner using social media data (Twitter).
Using the Aylien News API to conduct sentiment and date-time analysis to visualize recent insights about Generative AI
APPLICATION OF TEXT MINING AND SENTIMENT ANALYSIS ON 30 HOTELS/RESTAURANTS IN KAMALA THAILAND FROM THE TOURIST ACCOMMODATION REVIEWS DATASET
In this project I have done Web Scraping and Sentiment Analysis of Food Reviews.
Aspect Based Sentiment Analysis to extract aspects and their sentiment scores from customer reviews of different shopping apps that are scraped from play store reviews. This helps businesses to identify and analyze their customers' sentiment towards their brand and correct them accordingly.
twitter real-time sentiment analysis
This model scores the sentiment of text with a value between 0 ("negative") , 0.5 ("neutral") up to 1 ("positive"). This model was trained on reviews. These were truncated to a maximum of 200 words and only the 20,000 most common words in the reviews are used.
aspect-based sentiment analysis using syntactic parsing in python
Performed PySpark based text pre-processing including lemmatization, POS tagging and UDF functions on customer feedback. Computed and visualized sentiment score to identify areas of improvements.
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