INTEGRATING AI-BASED SENTIMENT ANALYSIS WITH SOCIAL MEDIA DATA FOR ENHANCED MARKETING INSIGHTS
Description
Sentimental analysis is a crucial
method for opinion mining in light of the everexpanding reach of social media and the
Internet. Recently research work describing the
performance of some common methods for
determining an individual's emotional state,
ranging from basic lexicon-and rule-based
approaches to deep learning algorithms. In this
study, an Artificial Neural Network (ANN)
model for sentiment classification on a Twitter
dataset with various data preprocessing,
feature extraction using TF-IDF and word
embeddings as well as performance evaluation.
It is observed that the ANN model surpasses
Naïve Bayes (95.98% accuracy) and K-Nearest
Neighbors (88.80% accuracy) on all evaluation
metrics, accomplishing an improvement in
accuracy (97.59%), precision (97.79%), recall
(98.64%), and F1 score (97.45%). ANN model
is fairly effective in capturing complex
sentiment patterns using TFIDF and
embedding features, has high classification
accuracy and overfitting is modest, therefore
leading to strong generalization to new data.
These findings validate ANN as a suitable tool
for decision-making based on sentiments to
enhance marketing, customer engagement and
brand reputation management.
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Additional details
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