Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

FastText Embedding and LSTM for Sentiment Analysis: An Empirical Study on Algerian Tweets

Record type:

paper
Creator:
LamSad
Publisher:
IEEE
Host:

Visit

doi.org

Tasks

sentiment analysistext classification

Languages

Arabic, Algerian Spoken

Licenses

https://doi.org/10.15223/policy-029https://doi.org/10.15223/policy-037

Similar

Sentiment analysis for Algerian Dialect tweetsTweets for sentiment analysisAn Optimized LSTM for Moroccan Darija Sentiment Analysis on Social MediaHybrid RNN-LSTM Architecture for Sentiment Analysis of Algerian Dialectal Social Media ContentAn Enhanced Feature Acquisition for Sentiment Analysis of English and Hausa TweetsSentiment Analysis on Naija-Tweets تحليل المشاعر على تغريدات نايجا Sentiment Analysis on Naija-Tweets Analyse des sentiments sur Naija-Tweets Análisis de sentimientos en Naija-Tweets

Sentiment analysis for Algerian Dialect tweets

Tweets for sentiment analysis

This is the dataset used in the research manuscript “Sentiment Analysis of Tweets: Political Climate

An Optimized LSTM for Moroccan Darija Sentiment Analysis on Social Media

Hybrid RNN-LSTM Architecture for Sentiment Analysis of Algerian Dialectal Social Media Content

Sentiment analysis in under-resourced dialects like Algerian Arabic (Darija) presents unique challen

An Enhanced Feature Acquisition for Sentiment Analysis of English and Hausa Tweets

Sentiment Analysis on Naija-Tweets تحليل المشاعر على تغريدات نايجا Sentiment Analysis on Naija-Tweets Analyse des sentiments sur Naija-Tweets Análisis de sentimientos en Naija-Tweets

Examining sentiments in social media poses a challenge to natural language processing because of the