ihabbendidi / sentiment_embeddings

A scientific benchmark and comparison of the performance of sentiment analysis models in NLP on small to medium datasets

Sentiment Analysis Benchmark

A scientific benchmark and comparison of the performance of sentiment analysis models in NLP on small to medium datasets

Authors : Ihab Bendidi, Yousra Bourkiche, Clément Siegrist, Kaouter Berrahal

In general, documents with similar sentiments, would be close to each other in the embeddings feature space. This can become another method to judge the performance of sentiment analysis models.

In this work, we aim to perform a benchmark of recent sentiment analysis works and models, reproduce their results, and judge their performance in comparison to baseline methods.

Outline

The following work in made on a jupyter notebook, that you can find here, or open in Colab here.

I - Processing & Exploratory Data Analysis

• Understanding the data
• Text Preprocessing

II - Sentiment classification models

• Bert Model
• LSTM recurrent model
• Baseline method : textblob

III - Document Embeddings

• Training doc2vec
• Doc2vec sentiment classifier

IV - Model performance visualisation

• Bert model
• LSTM model
• Logreg model
• Textblob

You can also find .pdfreport with code here.

Installation

This was tested on Ubuntu 20.04 with Python 3.7, but should run on any device and any python 3 version.

Before running it, make sure to install dependencies, by running in terminal :

pip install -r requirements.txt

On Google colab, you would need to import the requirements.txt file, and the tweets.csv dataset to your colab session.