AI mini projects of Building a Sentiment Analysis Model with Source Code.

 Artificial Intelligence (AI) has become an integral part of modern life. It is used in a variety of applications ranging from autonomous vehicles to facial recognition. One of the most popular applications of AI is the use of sentiment analysis.

Sentiment analysis is the process of identifying and extracting sentiment from text data. It is used to determine the emotional tone of a sentence or phrase. It is used to gain insights into customer preferences and opinions, as well as to detect potential problems in customer service.

In this article, we will discuss how to build an AI mini project of sentiment analysis model. We will walk through the process of building a machine-learning model using Python and the Scikit-learn library. We will then provide a source code example of a sentiment analysis model that can be used to classify a sentence or phrase into one of three categories: positive, neutral, or negative.

The first step in building a sentiment analysis model is to acquire a dataset. We will use a dataset of movie reviews from IMDB. The dataset consists of 5,000 movie reviews with labels indicating whether the review is positive (1), neutral (0), or negative (-1).

Once the dataset is acquired, the next step is to preprocess the data. This includes removing punctuation, stop words, and other unnecessary characters. We then tokenize the text, which means we break the sentences into individual words.

The final step is to train the model. We will use the Scikit-learn library to train our model. We will use a Support Vector Machine (SVM) classifier to classify the sentences. The SVM will take the tokenized words as input and predict the sentiment of the sentence based on the trained model.

The following is a source code example of a sentiment analysis model using Python and Scikit-learn. The example includes preprocessing, tokenizing, and training the model.

import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.svm import LinearSVC

# Load the dataset
dataset = np.loadtxt('imdb.txt', delimiter='\t')

# Preprocess the data
X = dataset[:, 0]
y = dataset[:, 1]

# Tokenize the text
vectorizer = CountVectorizer()
X_train = vectorizer.fit_transform(X)

# Train the model
clf = LinearSVC()
clf.fit(X_train, y)

# Make predictions
y_pred = clf.predict(X_train)

# Evaluate the model
accuracy = np.mean(y_pred == y)
print('Accuracy: %.2f' % accuracy)

With this source code example, you can build a sentiment analysis model with Python and Scikit-learn. The model can be used to predict the sentiment of a given sentence or phrase. This example can be extended to larger datasets or more complex models.