HARCOURT BUTLER TECHNICAL UNIVERSITY, KANPUR
Department of Computer Science & Engineering
Project Synopsis
Submitted in partial fulfillment of the requirements for the degree of
Bachelor of Technology
in
Computer Science & Engineering
Submitted by :S. No.
Name
Roll Number
1
2
3
4
Sahil Yadav
Dhara Raje Singh
Prakhar Mehrotra
Yogendra
230104051
230104021
230104038
230104071
Under the Supervision of
Dr. Raghuraj Singh
Department of Computer Science & Engineering
Harcourt Butler Technical University, Kanpur
Semester : VII
Signature:
Date:
Academic Session : 2026-2027
Sentiment Analysis Using Natural Language Processing
With the rapid growth of social media, e-commerce platforms, review websites, and online communication,
a huge amount of textual data is generated every day. This data contains valuable information about
people's opinions, emotions, and attitudes toward products, services, events, and topics.
Sentiment Analysis is a Natural Language Processing (NLP) technique used to automatically identify and
classify the sentiment expressed in a piece of text. The sentiment can generally be classified into categories
such as Positive, Negative, or Neutral.
The proposed project “Sentiment Analysis Using Natural Language Processing” aims to develop a system
that accepts textual input such as customer reviews, comments, or social media posts and determines the
sentiment expressed in the text. The system will perform various NLP preprocessing operations such as
tokenization, removal of stop words, normalization, and feature extraction before applying a machinelearning or deep-learning model for sentiment classification.
The system can be useful in areas such as customer feedback analysis, product review analysis, social
media monitoring, brand reputation analysis, and opinion mining.
Objective
The main objectives of the project are:
To develop a robust machine learning pipeline capable of ingesting raw, unstructured text data (such
as product reviews or social media feeds).
To implement comprehensive text preprocessing techniques, including tokenization, stop-word
removal, and lemmatization, to clean the data for computational analysis.
To utilize advanced word embedding techniques (such as GloVe or BERT) to map textual data into
high-dimensional vector spaces, capturing semantic relationships.
To train and evaluate classification algorithms (e.g., Support Vector Machines, LSTM, or Transformer
models) to accurately categorize the vectorized text into distinct sentiment polarities: Positive,
Negative, or Neutral.
To generate confidence scores for each classification and visualize the aggregated sentiment trends
through a user-friendly analytical report.
Motivation
The primary motivation for this project is the critical need for automated data analysis in the modern
digital economy. Human evaluation of thousands of reviews or comments is computationally impossible
and highly prone to subjective bias. By engineering an automated NLP pipeline, organizations can extract
actionable business intelligence in real-time, allowing them to monitor brand reputation, gauge public
reaction to legislative changes, and improve customer satisfaction with high statistical accuracy.
Data Flow Diagram
External Entities : User / Text Source: This is the origin point of the raw data. It could represent a human typing a review,
an automated web scraper pulling tweets, or a database feeding historic text into the system.
User / Data Consumer: This is the final destination. It represents the end-user, dashboard, or business
intelligence application that receives and interprets the finalized analytical reports.
Model Training: This represents a separate, offline machine learning environment. It takes in "New
Data" to continuously calculate and update the mathematical weights of the NLP algorithm, ensuring
the system remains accurate over time.
Data Processing Pipeline : Data Acquisition & Preprocessing: The system ingests raw text from the external user/source. The text
undergoes tokenization, stop word removal, stemming/lemmatization, and text normalization to
produce clean, preprocessed text.
Feature Extraction: The preprocessed text is passed through this module, which references external
lexicons and word embeddings to convert the human text into mathematical, vectorized data.
Sentiment Classification (NLP Model): The vectorized data is fed into the core machine learning
engine. Using trained model parameters, the algorithm calculates the most probable sentiment of the
numerical arrays.
Sentiment Analysis Result Generation: The mathematical output is translated into a definitive
Sentiment Label (Positive/Negative/Neutral) alongside statistical Confidence Scores.
Output Generation / Visualization: The labels and scores are aggregated and delivered to the enduser or data consumer as structured Sentiment Reports and Analytics.
Work Progress
References
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of
deep bidirectional transformers for language understanding
Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and
applications: A survey
Pennington, J., Socher, R., & Manning, C. D. (2014). GloVe: Global vectors for word
representation
Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis
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