INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue IV, April 2025

www.ijltemas.in Page 466

Smart Decisions with Opinion Mining

Dinesh M

Vels Unversity, India

DOI : https://doi.org/10.51583/IJLTEMAS.2025.140400047

methods to classify opinion as positive, negative, or neutral. In this paper, sentiment analysis of social media data is investigated

based on a Twitter dataset, utilizing machine learning methods such as Long Short-Term Memory (LSTM) networks for precise

sentiment classification.

Keywords: Web technology, social networking sites, Twitter, Facebook, Sentiment analysis, Opinion extraction, public sentiment,

trend monitoring, business strategy, customer satisfaction, machine learning, long short-term memory (LSTM) networks.

I. Introduction

The internet age has revolutionized the way individuals voice opinions via blogs, forums, reviews, and social media. Millions utilize

sites such as Facebook and

Twitter to voice opinions and sway others. Social media creates huge emotional data in the form of posts, comments, and reviews,

offering businesses a chance to connect for decisions. Such as reading reviews prior to buying. The sheer amount of data requires

as line chart, bar chart and histograms with customization option. nlkt – An advanced NLP library that provides tools for

tokenization, stemming, lemmatization, stop word elimination, and part-of-speech tagging for sentiment analysis and linguistic

studies. Nltk.corpus – offers access to large corpora of languages such as Brown Corpus, Guntenberg Corpus, and WorldNet that

can be helpful in text categorization and syntactic parsing. Nltk.stem – Contains stemmers such as Porter and Lancaster to cut down

on words to their base form for search engines and text normalization purposes. Nltk.tokenize – Divide text into words or sentence

effectively with support for different languages and types. NumPy - A core package for numerical computing. Multi-dimensional

array linear algebra and mathematical functions supported. Pandas- Data analysis and manipulation library with data frame and

series structure for statistical analysis and efficient data handling. Sklearn.metrics – Offers evaluation metrics of classification,

regression, and clustering models to retrieve a machine learning performance. Sklearn.model selection – Library for dataset

splitting, cross-validation and hyperparameters tuning such as train set split and GridSearchCV. TensorFlow – Deep learning library

Sentiment Analysis pf Twitter Data (Asafuzzaman et al., 2020) – Examines Sentient analysis methods on Twitter, such as lexicon

based and machine learning approaches [1]. Deep Sentiment Analysis Learning (Zhang et al., 2018) – Focuses on deep learning

model, (CNNs and RNNs) in sentiment analysis and importance of feature, extraction, and pre-trained embeddings such as

world2Vec [2]. Comparative Study of sentiment analysis Methodologies (Hossain en al., 2019) – Compares naïve Bayes, SVM,

deep learning models and concludes deep learning in superior and preprocessing increases the performances [3]. Sentiment

Classification Using Machine learning (Kumar et al., 2021) – Discusses supervised and focusing hybrid methods for enhanced

accuracy [4].

Exploring Sentiment Analysis for social media (Gupta et al., 2020) – Focusing on rule-based and machine methods, highlighting

the effect of linguistic features such as hashtags and emoticons [5]. Novel Deep Learning Approaches for Sentiment Analysis

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue IV, April 2025

www.ijltemas.in Page 467

(Ramesh et al., 2022) – Suggests an LSTM- based attention mechanism to enhance classification accuracy in twitter data [6]. Real-

II. Methodology

Sentiment analysis employs NLP to draw out opinions, attitudes, and emotions from text, speech, or databases. Also referred to as

opinion mining, it categorizes sentiments into positive, negative, or neutral. Twitter Sentiment Analysis has been researched using

binary classification via Naïve Bayes, Maximum Entropy, and SVM, with SVM usually being the best performer. Machine learning

models have been experimenting with features such as unigrams, hashtags, and the bag-of-words model. Scientists have developed

techniques to differentiate subjective and objective tweets as well as identifying emotions through WordNet. Spam, among other

challenges, has been addressed, and techniques such as stochastic gradient descent and k-nearest neighbors have been applied with

mixed success.

Natural Language Processing Algorithms

The algorithm reads the text and divides it based on defined delimiter (e.g., spaces, punctuation)

Advantages

Simplifies text processing by breaking it into manageable pieces.

Disadvantages

May struggle with contractions or compound words if not properly configured.

Word Embeddings

Overview

Word embeddings are dense vector representations of words that capture semantic meaning based on their context within a corpus.

How It Works

Algorithms such as Word2Vec or Glove learn associations from large text corpora and represent words as vectors in a continuous

Overview

Part-of-speech Tagging involves assigning parts of speech (e.g., noun, Verb) to each word in a sentence.

How it Works

Algorithms employ statistical models or rule-based systems to analyze sentence structure and identify word roles.

Advantages

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

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Improves understanding of grammatical relationships in sentences.

Disadvantages

Might not perform well uncertain words without enough context.

Data Preprocessing Methods

Data preprocessing plays an important role in making raw data ready to be analyzed and modelled. The following are the primary

methods that usually come into the picture:

Introduction

Reducing words into their root or base form are done using methods like stemming and lemmatization.

How It Works

Stemming: Trims off prefixes to reach a base form (e.g., “running” is reduced to “run”).

Lemmatization: Applies vocabulary and morphological analysis to return the base form (e.g., “better” is reduced to “good”).

Feature Extraction

Overview

Feature Extraction converts raw data into numerical features that are acceptable for machine learning models.

How It Works

Methods such as Term Frequency – Inverse Document Frequency (TF-IDF) transform text into numerical vectors by words

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue IV, April 2025

www.ijltemas.in Page 469

This process of sentiment analysis begins with user input, where tweets are gathered and preprocessing. Feature extraction, after

it is subjected to the same features extraction process prior to being inspected by the trained classifier. From the extracted features,

the classifier makes a prediction of whether the sentiment of the tweets is positive or negative. This machine learning based

into people’s opinions.

Sentiment Classification System Based on Emoticons

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue IV, April 2025

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The figure depicts the sentiment classification process Twitter emoticons. It starts with gathering tweets via the Twitter Streaming

Lexicon-Based Model

The Lexicon-Based Model processes sentiment according to predefined lists of word. Preassembled and general word lists are

combined into a lexicon that is then used on tokenized document. Sentiment is scored from the words, and the polarity (positive,

analysis.

Sentiment Analysis Tasks

the opinions as being positive, negative, or neutral. This area being positive, negative, or neutral. These are the processes that

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue IV, April 2025

www.ijltemas.in Page 471

The figure shows the various levels of sentiment analysis, which is the process of classifying the sentiment or emotion conveyed in

a text. Sentiment analysis may be carried out at more than one level, such as word-level-sentiment analysis, where the sentiment of

words is analysed; sentence-level sentiment analysis, where the overall sentiment of a sentence is analysed; document-level

sentiment analysis, where the sentiment of an entire document is assessed; and feature-based sentiment analysis, where an emphasis

is put on sentiment extraction based on aspects or features within the text. These various level assist in the deeper comprehension

of expressed emotions and opinions contained in text data, which makes sentiment analysis an essential resource in marketing,

customer opinion analysis, and social media tracking.

III. Conclusion

We effectively processed text data for sentiment – positive, negative, or neutral – via NLP, data preprocessing, and machine

learning. Our findings identify the importance of sentiment analysis in business, customer reviews, and social media. Accuracy in

the model relies on data quality, feature engineering, and algorithm choice. Future enhancement involves deep learning (e.g.,