Sentiment Analysis ML: Harnesses natural language processing to analyze sentiment. Trained on labeled text data, an ML model detects emotions in real-time. Tracks sentiment and provides insights. Showcasing AI's sentiment analysis abilities for diverse applications.
#Sentiment_Analysis GUI Sentiment Analysis Python Imports This repository contains a Python script for sentiment analysis. The script utilizes various libraries and modules to perform the analysis. Here are the imports used in the script:
pandas: A library for data manipulation and analysis. It is used for reading and processing data from CSV files. sklearn.feature_extraction.text.CountVectorizer: A module from scikit-learn library for converting text into numerical features using the Bag-of-Words approach. sklearn.model_selection.train_test_split: A module from scikit-learn library for splitting data into training and testing sets. sklearn.naive_bayes.MultinomialNB: A module from scikit-learn library implementing the Multinomial Naive Bayes classifier. sklearn.metrics.accuracy_score: A module from scikit-learn library for evaluating the accuracy of classification models. nltk: The Natural Language Toolkit, a library for natural language processing tasks. nltk.corpus.stopwords: A module from NLTK providing a list of common stop words to remove from text data. nltk.tokenize.word_tokenize: A module from NLTK for tokenizing text into individual words. matplotlib.pyplot: A module for creating visualizations such as plots and charts. matplotlib.image: A module for working with images in Matplotlib. string: A module providing a collection of string constants and utility functions. joblib: A library for saving and loading Python objects, used here to load a pre-trained classifier model. warnings: A module for managing warning messages. sys: A module providing access to system-specific parameters and functions. These imports are necessary for various aspects of the sentiment analysis script, including data processing, feature extraction, model training, evaluation, and visualization.
Please note that in order to run the script, you may need to install the required libraries and have the necessary data files or models available.
For more details, refer to the code implementation and comments within the script itself.