CERTIFICATE
This is to certify that ADITYA PRADHAN student of Class
XII Sc has successfully prepared the report on the Project
entitled “SENTIMENT ANALYSIS ON TEXT USING
AI/ML” under the guidance of Mr. RAMA CHANDRA
DAS SIR.
The report is the result of his efforts & endeavours. The report
is found worthy of acceptance as final Project report for the
subject Computer Science of Class XII Sc.
Signature of Signature of
Internal Teacher External Teacher
Signature of Principal
ACKNOWLEDGEMENT
I would like to express a deep sense of thanks and gratitude to
my project guide Mr. RAMA CHANDRA DAS SIR for
guiding me immensely through the course of the project. He
always envinced keen intrest in my project. His constructive
advice & constant motivation have been responsible for the
successful completion of this project.
My sincere thank goes to our principal sir for his co-
ordination in extending every possible support for the
completion of this project.
I must thanks to my classmates for their timely help and
support for completion of this project.
Last but not the least, I would like to thank all those who had
helped directly or indirectly towards the completion of this
project.
INTRODUCTION TO SENTIMENT
ANALYSIS
Sentiment analysis, also known as opinion mining, is a field of natural
language processing (NLP) that aims to understand and extract
subjective information from text data. It analyzes the emotional tone
or sentiment expressed in a piece of text, categorizing it as positive,
negative, or neutral.
Sentiment analysis has become increasingly popular due to its wide
range of applications, including:
• Social media monitoring: Tracking public opinion about brands,
products, and events.
• Customer feedback analysis: Identifying customer satisfaction
levels and areas for improvement.
• Market research: Understanding consumer preferences and
market trends.
• Political analysis: Assessing public sentiment towards political
candidates and policies.
This project will delve into the practical aspects of building a sentiment
analysis system using Python, exploring various AI/ML techniques to
achieve accurate and robust sentiment classification.
DATA COLLECTION AND
PREPROCESSING
The first step in any sentiment analysis project is to gather a relevant
dataset. This dataset should contain text data labeled with the
corresponding sentiment, such as positive, negative, or neutral.
There are several sources for obtaining such datasets, including:
• Publicly available datasets: Websites like Kaggle and UCI
Machine Learning Repository offer pre-labeled datasets for
sentiment analysis tasks.
• Web scraping: Extracting data from websites and social media
platforms using libraries like Beautiful Soup and Scrapy.
• API access: Utilizing APIs provided by platforms like Twitter and
Amazon to access user reviews and tweets.
Once the data is collected, it needs to be preprocessed to prepare it
for model training. This involves:
• Cleaning the data: Removing irrelevant characters, punctuation,
and stop words (common words like "the", "a", "is").
• Tokenization: Breaking down text into individual words or
phrases (tokens).
• Stemming or lemmatization: Reducing words to their root form
to normalize variations.
• Feature extraction: Creating numerical representations of the
text data, such as bag-of-words or TF-IDF.
This preprocessing step is crucial to ensure that the model receives
clean and meaningful data for learning.
MODEL SELECTION AND
TRAINING
With the preprocessed data ready, we can select a suitable machine
learning model for sentiment classification. Several models are
commonly used, including:
• Naive Bayes: A probabilistic model that uses Bayes' theorem to
classify text based on word frequencies.
• Support Vector Machines (SVM): A supervised learning model
that finds the optimal hyperplane to separate data points into
different classes.
• Logistic Regression: A statistical model that predicts the
probability of a text belonging to a particular sentiment class.
• Deep Learning Models: Techniques like Recurrent Neural
Networks (RNNs) and Long Short-Term Memory (LSTM) are
particularly effective for capturing context and dependencies in
text data.
The choice of model depends on factors like the dataset size,
complexity of the task, and available resources. Once the model is
chosen, it needs to be trained on the labeled data. This involves
adjusting the model's parameters to minimize errors and improve its
ability to predict sentiment correctly.
The training process typically involves iterative optimization
algorithms, like gradient descent, to find the optimal model
parameters. The goal is to achieve a high level of accuracy on the
training dataset, which is a measure of the model's ability to learn
from the provided data.
