Sentiment-Analysis-by-combining-Machine-Learning-and-Lexicon-Based-methods

This project is on twitter sentimental analysis by combining lexicon based and machine learning approaches. A supervised lexicon-based approach for extracting sentiments from tweets was implemented. Various supervised machine learning approaches were tested using scikit-learn libraries in python and implemented Decision Trees and Naive Bayes techniques.

The entire code for preprocessing, implementation and post-processing of the project was done in Python 2.7.

Overview of the Project

Requirements

The packages required for running the code are listed below.

• Sklearn
• Pandas
• Numpy
• Math
• io
• os
• Nltk

Installations

Most of the packages can be installed using normal pip commands. Installing NLTK may require special instructions which can be found at https://www.nltk.org/install.html

The preprocessing files which are required to run the code are as follows:

1. tweetylabel.csv #contains the input tweets
2. dic.csv #contains the dictionary created and merged
3. intense.csv. #contains the intensifiers
4. bucket.csv. #creates the bucket
5. positive-words.txt #contains the positive word list as text file
6. negabuse.txt #contains the negative and abusive word list as text file

Instruction for running the code

Keep all the above mentioned preprocessing files in the same folder and change the directory to that folder. lexi_plus_ml.py file contains the entire code for the project. Open the code and specify the working directory on line 17 of the code.