https://github.com/lucasfranklinsilva/analise-de-sentimentos
Análise de Tweets com NLTK e Bloob.
https://github.com/lucasfranklinsilva/analise-de-sentimentos
analysis bloob-microservice machine-learning nltk python tweets
Last synced: 2 months ago
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Análise de Tweets com NLTK e Bloob.
- Host: GitHub
- URL: https://github.com/lucasfranklinsilva/analise-de-sentimentos
- Owner: lucasfranklinsilva
- Created: 2018-12-24T02:28:12.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2018-12-24T02:33:16.000Z (over 6 years ago)
- Last Synced: 2025-03-24T22:42:01.745Z (3 months ago)
- Topics: analysis, bloob-microservice, machine-learning, nltk, python, tweets
- Language: Jupyter Notebook
- Homepage:
- Size: 99.6 KB
- Stars: 20
- Watchers: 0
- Forks: 8
- Open Issues: 0
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# [PT] Análise de Sentimentos - Processamento de Linguagem Natural
Esse projeto consiste na Análise de sentimentos de tweets coletados através da API do Twitter sobre as eleições presidencias de 2018 no Brasil. O projeto consiste em:
1. Principais tweets coletados no período de 1 semana.
2. Tweets mais curtidos e retweetados.
3. Fonte dos tweets.
4. Análise de sentimentos em Português utilizando como set de treinamento o Corpus ReLi. (https://www.linguateca.pt/Repositorio/ReLi/)
5. Comparação entre resultados das bibliotecas Bloob e NLTK.
6. Nuvem de palavras mais frequentes.
7. Séries de tweets no tempo.
8. Mapa de tweets utilizando a localização declarada pelos usuários.
# [EN] Sentiment Analysis - Natural Language Processing
[EN] This project consists in the Analysis of sentiments of tweets collected through the Twitter API on the presidential elections of Brazil in 2018. The project consists of:
1. Top tweets collected in 1 week period.
2. Tweets more liked and retweet.
3. Source of tweets.
4. Analysis of sentiments in Portuguese using Corpus ReLi as training set. (https://www.linguateca.pt/Repositorio/ReLi/)
5. Comparison between Bloob and NLTK librarys.
6. Cloud of most frequent words.
7. Tweets Time Series.
8. Map of tweets using the location declared by users.