Abstract:
The fast growth of online platforms has made
the volume of user-generated content grow enormously in the form of reviews,
comments, and feedback. Extracting meaningful information from such
unstructured text data is a major challenge in the area of NLP. The aim of this
project is to implement an AI-based sentiment analysis model that classifies
movie reviews as positive or negative, thus automating the understanding of
public sentiment. The proposed solution uses the IMDb Movie Reviews dataset,
which comprises 50,000 labelled movie reviews, for training and testing a deep
learning model with respect to binary sentiment classification. Further, the AI
model is created with the use of a Long Short-Term Memory network, also called
an RNN; because it has captured the long-term dependencies of text, this was
especially suitable for handling sequential data. The critical stages of model
development were the pre-processing of data, tokenization of data, padding of
sequences, and the implementation of word embedding’s to represent words in
continuous vector space. Later, the LSTM model was trained on the pre-processed
dataset optimized by using the Adam optimizer and evaluated against the
reserved test set regarding the performance of the model.
Accuracy achieved by the LSTM model on the
test dataset is about 87%, showing very good performance concerning the task of
sentiment prediction using movie reviews. That means the performance metrics
give an indication of good generalization capability of the model towards new
unseen data, becoming reliable for real-world sentiment analysis applications.
These findings are an implication that LSTM-based models have immense potential
for a wide range of applications, especially in sentiment analysis, like
customer feedback analysis, social media monitoring, and market research.
Despite such high scores on all three
metrics, this model can still be fine-tuned further, for example, with the help
of attention mechanisms or transformer-based models like BERT, which will allow
the model to capture subtler variations of linguistic patterns. Another
promising direction involves sophisticated data augmentation with a view to
increase diversity in training data and, hence, mitigate overfitting. In
general, the project has shown the viability of deep learning for sentiment
analysis and given a simple idea of how to develop other solutions concerning
NLP.
Keywords:
1.
Artificial Intelligence (AI)
2.
Deep Learning
3.
Convolutional Neural Networks (CNN)
4.
Model Training
5.
Validation Accuracy
6.
Model Evaluation
7.
Overfitting
8.
Hyperparameter Tuning
9.
Regularization Techniques
10.
Generalization
11.
Test Accuracy
12.
Loss Functions
13.
Data Preprocessing
14.
Machine Learning Optimization
15.
Performance Metrics
Table of contents:
|
1. Introduction………………………………………………………………………5 2.
Literature Review……………………………………………………………….6 3.
Methodology/Approach…………………………………………………………8 4.
Implementation………………………………………………………………….11 5.
Results & Discussion………………………………………………………….13 6.
Conclusion & Future Directions………………………………………………14 7.
References……………………………………………………………………15 |
1.Introduction
Artificial
Intelligence is now a transformational technology in several verticals,
automating processes to make machines perform tasks that typically would
require human intelligence. AI in healthcare helps diagnose diseases, creates
personalized treatment plans, and manages patient data. AI algorithms in
finance analyse market trends, identify fraudulent activities, and execute
trades automatically. Similarly, in the automotive industry, AI is applied for
autonomous driving, while retail makes use of AI in personalized
recommendations, demand forecasting, and inventory management. AI enables analysing
vast volumes of data, recognizing patterns, and making predictions that have
wide applications in natural language understanding tasks, such as sentiment
analysis, which is critical to understand customer behaviour and brand reputation,
and hence public sentiment.
Real-World Scenario/Problem
This
is in response to exponential growth in user-generated text on digital
platforms such as social media, e-commerce websites, and review aggregators.
These, therefore, call for the intervention of automated systems that make
sense of such unstructured text data. Sentiment analysis, also known as opinion
mining, is one such application that hopes to determine the sentiment of a
given text, whether positive, negative, or neutral. It will focus on the
sentiment analysis of movie reviews from the IMDb platform. It is envisioned
that the sentiment of movie reviews may help a film studio comprehend audience
reception, guide marketing strategies, and also provide the consumers with an
enhanced decision-making tool. With such a vast amount of data and complexity
of natural language, it is impossible to perform this manually; therefore, AI-based
solutions are required.
Aims and Goals
The
main aim of this project is to build an AI sentiment analysis model that can
predict the movie review as positive or negative. Hence, the main aims to be
achieved during the project are as follows:
· Pre-processing and cleaning the text data to
make it suitable for analysis.
· It will develop a deep learning model by
implementing an LSTM network capable of handling sequential data, hence
capturing the contextual essence of words within sentences.
· To appraise the performance of the model using
appropriate metrics: accuracy, precision, recall, and F1-score.
· It shall highlight and discuss any loopholes
that the model may have and give recommendations for possible future
improvement.
AI
Solution
In
this solution, the AI proposed will involve an LSTM-a type of RNN, which is
particularly better positioned for finding patterns and long-term dependencies
expected in sequential data. Since this model design will allow the LSTM to be
deeply sensitive to context and sentiment expressed through movie reviews, this
is suitable for sentiment analysis. That would include some key steps in the
solution: data pre-processing involving tokenization, removal of stop-words,
and padding; the creation of word embedding’s that map the textual data into a
continuous vector space; and the training of the LSTM model on the pre-processed
data. For this, model performance is measured on a test dataset to ensure that
the model generalizes well for new unseen data. The proposed system will try to
help stakeholders in the decision-making process with more data-driven insights
into the comprehension of public sentiments by automating the sentiment
classification.
2.Literature Review
The
area of interest in sentiment analysis has been one of the research foci within
the domain of NLP due to its wide domain applications in marketing, customer
service, finance, and social media monitoring. Earlier approaches in sentiment
analysis involved lexicon-based methods, which were based on pre-defined lists
of positive and negative words. These techniques worked to a certain extent but
suffered from the understanding of context, sarcasm, and negations. Hence, to a
large extent, they were quite limited in their accuracy. The use of machine
learning and deep learning approaches has truly revolutionized the domain of
sentiment analysis, given an approach that is more accurate due to being driven
through data.
Sentiment
analysis has been carried out using different methods such as tradition machine
learning techniques like Support Vector Machines and Naive Bayes to advanced
deep learning models such as Convolutional Neural Networks and Recurrent Neural
Networks. Sentiment classification tasks are usually performed on the basis of
machine learning using methods such as Naive Bayes, Maximum Entropy, and SVM.
Very effective results have been shown in earlier experiments by (Pang et al.
2002). The introduction of word embedding’s, such as Word2Vec and GloVe, channelled
deep learning models and raised attention in the community of NLP researchers.
Word embedding’s capture semantic relationships between words, putting the
model in a better position to understand context within sentences.
Current Trends
Current
advances in deep learning, especially the use of LSTMs and GRUs, have given
encouraging results in performing sentiment analysis tasks. LSTM networks
recently proposed by Hochreiter and Schmidhuber 1997 are capable of capturing
long-range dependencies in texts. This situates them if applied in sentiment
analysis since the sentiment of a review may be dependent on the context set by
previous words. Recent studies, such as by Zhou et al. 2016, demonstrated that
the LSTM network is more effective for tasks of text classification, especially
when handling long and complex sentences.
Transformer-based
models have gone even further. BERT was developed by Devlin et al. in 2018.
These models extended NLP even more as they enabled attention of a model to one
part of the sentence when it is trying to predict an outcome. Models such as
these have been hugely successful with state-of-the-art results in sentiment
analysis beyond those earlier RNN-based models. However, they do come at a high
computational cost, which may not always be feasible in applications.
Research Gaps
Although
LSTM and transformer-based models performed well in sentiment analysis, there
are still some gaps that the research project at hand seeks to fill. An
important gap that this work is likely to fill is in the devising of more
effective models that strike the right balance between achieving optimal
performance results and maintaining computational efficiency to a state where
these models can effectively be used for practical deployment. In addition,
many of the pre-existing models lack mechanisms that efficiently tackle the
problems of sarcasm, a change in context, and domain-specific usage of jargon,
which largely affect the performance of sentiment classification. This project
fills that gap by the optimized implementation of the LSTM model and explores
techniques for its better understanding of complex linguistic patterns.
The
review is aimed to build the background of the proposed AI-based sentiment
analysis model in relevance to the advancement of the domain by proffering a
practical solution for real-world applications.
3.Methodology/Approach
The most significant technique of AI that has
been adopted in the proposed project is an LSTM network, belonging to a broader
category of RNNs. The LSTM networks are essentially designed for handling
sequential data and can learn long-term dependencies, thus making this
architecture quite suitable for natural language processing tasks, especially
sentiment analysis. Unlike traditional RNNs, LSTMs use memory cells and gating
mechanisms that will enable them to keep information over really long
sequences, hence understanding context within sentences better. This is very
important for sentiment analysis, where sentiments may be expressed depending
on the context set by earlier words or phrases in a review.
The use of LSTM was supported because it had
previously shown excellent performance on text classification tasks, especially
in capturing semantic relations and fixing the vanishing gradient problems
compared to traditional RNNs. Besides, LSTMs offer a good balance of
computational efficiency with model performance as compared to complex models
like transformers; hence, applications which have restricted computational
resources can access them.
Data Collection and Pre-processing
Data Source:
The dataset used for this project is the IMDb
Movie Reviews dataset, taken from Kaggle. The data contains 50,000 labeled
movie reviews, evenly split into positive and negative sentiment classes,
making it highly ideal for binary sentiment classification tasks.
Data Collection:
The dataset was downloaded from Kaggle and
was loaded into Python through the pandas library. The data was already labelled
in terms of "positive" or "negative" sentiments, so no extra
labelling of data was required.
Data Pre-processing
Steps:
Text Pre-processing:
The text data has been cleaned from HTML tags
and special characters, and anything non-alphabetical to have the model focus
on the actual textual content. This has been made possible through regular
expressions.
Tokenization:
Cleaning of text data was done through
tokenization whereby each word had been given a unique integer. This is a very
crucial step for converting the text data into a format that could be fed into
the neural network.
Padding Sequences:
Considering the fact that all reviews have
different lengths, to handle these, all tokenized sequences were padded to a
fixed length of 200 words by using the pad_sequences function provided in
Keras. In this way, all inputs that feed into the LSTM network have unified
sizes in their dimensions.
Word Embedding:
Word embedding’s have been used to project
the words into a continuous vector space, by which semantic relations between
words are preserved. Thus, in this work, word embedding’s are generated using
an Embedding layer in Keras that is pre-specified with a vocabulary size and
embedding dimension.
Train-Test Split:
The paper conducts an 80-20 split for
training and testing, respectively, to train the model on it and further test
its performance on unseen data.
Evaluation and Analysis
In this regard, the LSTM model was trained
using the Adam optimizer. This is one of the most used optimizers with deep
learning models due to its adaptive learning rate and computational efficiency.
Besides, some of the key performance metrics that have been used to evaluate
model performance include:
Accuracy:
A measure of the percentage of correct
predictions concerning the total number of predictions done by the model.
Accuracy of 87% was realized on the test dataset, therefore, meaning that the
model can differentially identify whether sentiment expressed in positive or
negative ways.
Precision, Recall, and
F1-score:
These were computed as a way to further give
a detailed evaluation of the performance of this model, especially on handling
imbalanced datasets. Precision measures the ratio of positive identifications
out of the total number of correct positives; recall is a measure of the actual
positives identified correctly out of the total number of actual positives,
while the F1-score is the harmonic mean of precision and recall.
Confusion Matrix:
The confusion matrix was plotted to visualize
the model's consumer performance in respect to true positives, true negatives,
false positives, and false negatives to understand where the model fits well
and where it needs an upgrade.
4.Implementation
Below are the steps followed for its
implementation:
5.Results and Discussion
Model Training
Performance:
The model is trained by running 2 epochs with
a batch size of 32. The training details are as follows:
Epoch 1:
The model has reached a training accuracy of
90.45% with a loss of 0.2481. The validation accuracy was 87.25% with a
validation loss of 0.3071.
Epoch 2:
The training accuracy increased to 92.79%,
while the training loss reduced to 0.1987. In contrast, the validation accuracy
slightly dropped to 86.90% with a validation loss of 0.3202.
These results indicate good model performance
on the training data, but there is a slight overfitting to the training data,
as evidenced by the increase in validation loss from 0.3071 to 0.3202, probably
due to the slight fall in validation accuracy after the first epoch.
Model Performance of
Evaluation:
The model was then tested against the test
dataset after training. Here are the results for the test:
Test Loss: 0.3202
Test Accuracy: 86.98%
This test accuracy of 86.98% is fairly close
to the validation accuracy that resulted during training; this in turn means it
generalizes reasonably well onto unseen data. On the other hand, the value for
test loss suggests there is more that can be improved by tuning the hyper
parameters or increasing the epochs to avoid overfitting and hence achieve
better generalization.
Model Insights:
This training process is a representation of
the pattern of increased accuracy with decreased loss values, which indicates
the normal learning curve of the model. However, given the small decline in the
validation performance after the first epoch, it suggests that the model starts
to memorize the data rather than generalize from the data.
One would be able to take care of this by
techniques like regularization-dropout, L2 regularization, early stopping, or
by increasing the size of the dataset in order for it to have more diverse
examples while training.
6.Conclusion and Future
Directions
The performance of the model came good by
getting a test accuracy of 86.98%, which is good enough to begin with. Still,
there is minor evidence of overfitting, as observed from the minor increase in
validation loss and decrease in validation accuracy between epochs.
Further optimization may be done through
tuning the number of epochs, performing regularization, or tuning the learning
rate to get the better tradeoff between the model's training and validation.
Overall, the model has potential, but much experimentation
and further optimization is needed to make it more strong.
The following directions for further research
and improvements could be followed after the results of this project:
Attention Mechanism
Incorporation: Attention mechanisms might help the model focus on those parts of the
review that are indicative of sentiment and should help deal with complex
linguistic structures better, as well as their context shifts.
Utilization of
Transformer-Based Models: The use of models like BERT or GPT from the transformer family could
also help in enhancing performance since these models have state-of-the-art
results for many NLP tasks by harnessing the power of contextual understanding
with the attention mechanism.
Data Augmentation and
Diversification: Data augmentation through strategies like paraphrasing or synonym
replacement will make the training data more diversified and attack the problem
of overfitting. It will enhance the generalization property of the model.
Domain Embedding’s: Domain-specific
pre-trained word embedding’s or further fine-tuning of general embedding’s on
the dataset could enhance the model's understanding of context-specific
language nuances, especially in movie reviews.
Handling Nuanced
Sentiments: Another possible extension of the current work could be with an eye
toward handling sarcasm, irony, and mixed sentiments within a single review.
This can be achieved either by incorporating more features into consideration
or by training on ever-more-nuanced datasets.
This present project confirms the efficiency
of LSTM-based models in sentiment analysis and serves to open a venue for
further research to develop on these findings in creating such advanced NLP
solutions.
8.References:
·
Chollet, F. (2018). Deep Learning with Python. Manning Publications.
·
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning.
MIT Press.
·
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature,
521(7553), 436-444. https://doi.org/10.1038/nature14539
·
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual
Learning for Image Recognition. In Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition (CVPR), 770-778.
https://doi.org/10.1109/CVPR.2016.90
·
Kingma, D. P., & Ba, J. (2015). Adam: A Method for Stochastic
Optimization. In International Conference on Learning Representations (ICLR).
https://arxiv.org/abs/1412.6980