Top 5 Unknown Sentiment Analysis Projects On Github To Help You Through Your NLP Projects (Includes links to Repository on Github)

Sentiment analysis refers to natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and personal information. Wikipedia

Sentiment analysis (or opinion mining) is a natural language processing technique used to determine whether data is positive, negative, or neutral. Sentiment analysis is often performed on textual data to help businesses monitor brand and product sentiment in customer feedback and understand customer needs.

In today’s article, we are going to talk about five 5 Unknown Sentiment Analysis Projects On Github To Help You Through Your NLP Projects to enhance your skills in the field of data science and machine learning.

Note: In this article, we are going to talk about some ‘low ball’ but really good open-source Sentiment Analysis projects which you can use in your projects. To read more about each of them I recommend following the link given along the project.

Bonus

Awesome Sentiment Analysis: A curated list of Sentiment Analysis methods, implementations and misc.

This repository aims to provide adequate links for scholars who want to research in this domain; and, at the same time, be sufficiently accessible for developers who wish to integrate sentiment analysis into their applications.

Github Repository

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Coming back to the topic -

1. Twitter Sentiment Analysis

Github

Twitter Sentiment Analysis is a general natural language utility for Sentiment analysis on tweets using Naive Bayes, SVM, CNN, LSTM, etc.

They use and compare various methods for sentiment analysis on tweets (a binary classification problem). The training dataset is expected to be a csv file of type tweet_id,sentiment,tweet where the tweet_id is a unique integer identifying the tweet, sentiment is either 1 (positive) or 0 (negative), and tweet is the tweet enclosed in "". Similarly, the test dataset is a csv file of type tweet_id,tweet. Please note that csv headers are not expected and should be removed from the training and test datasets.

There are some general library requirements for the project and some which are specific to particular methods. The general conditions are as follows.

• numpy
• scikit-learn
• scipy
• nltk

The library requirements specific to some methods are:

• keras with TensorFlow backend for Logistic Regression, MLP, RNN (LSTM), and CNN.
• xgboost For XGBoost.

Note: It is recommended to use Anaconda distribution of Python.

2. Pytorch Sentiment Analysis

Github

Pytorch Sentiment Analysis is a repository containing tutorials covering how to perform sentiment analysis using PyTorch 1.7 and torchtext 0.8 using Python 3.8.

The first two tutorials will cover getting started with the de facto approach to sentiment analysis: recurrent neural networks (RNNs). The third notebook covers the FastText model, and the final covers a convolutional neural network (CNN) model.

There are also two bonus “appendix” notebooks. The first covers are loading your datasets with TorchText, while the second contains a brief look at the pre-trained word embeddings provided by TorchText.

Table of contents:

• 1 — Simple Sentiment Analysis
• 2 — Upgraded Sentiment Analysis
• 3 — Faster Sentiment Analysis
• 4 — Convolutional Sentiment Analysis
• 5 — Multi-class Sentiment Analysis
• 6 — Transformers for Sentiment Analysis
• A — Using TorchText with your Datasets
• B — A Closer Look at Word Embeddings
• C — Loading, Saving and Freezing Embeddings

3. Senta

Github

Senta is a python library for many sentiment analysis tasks. It contains support for running multiple tasks such as sentence-level sentiment classification, aspect-level sentiment classification and opinion role labelling.

The bulk of the code in this repository is used to implement SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis.

The paper demonstrates how to integrate sentiment knowledge into pre-trained models to learn a unified sentiment representation for multiple sentiment analysis tasks.

You can directly use the Python package to predict sentiment analysis tasks by loading a pre-trained SKEP model.

python -m pip install Senta

or

git clone https://github.com/baidu/Senta.git
cd Senta
python -m pip install .

4. Text-Analytics with Python

Github

Text-Analytics with Python helps you learn how to process, classify, cluster, summarize, understand syntax, semantics and sentiment of text data with the power of Python! This repository contains code and datasets used in my book, “Text Analytics with Python,” published by Apress/Springer.

“Text Analytics with Python” is a book packed with 674 pages of useful information based on techniques, algorithms, experiences and various lessons learnt over time in analyzing text data. This repository contains datasets and code used in this book.

5. LSTM Sentiment Analysis

Github

LSTM Sentiment Analysis is a repository that contains the iPython notebook and training data to accompany the O’Reilly tutorial on sentiment analysis with LSTMs in Tensorflow.

Note: See the original tutorial to run this code in a pre-built environment on O’Reilly’s servers with cell-by-cell guidance, or run these files on your own machine.

Another file called allows you to input your text and see the output of the trained network.

To run the iPython notebook, you’ll need the following libraries.

• TensorFlow version 1.1 (See below for later versions)
• NumPy
• Jupyter
• matplotlib

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