Sentiment Analysis with Different Deep Learning Methods
Zixiang Chen
School of Software Engineering, Chongqing University of Post and Telecommunications, Chongqing, 400065, China
Keywords: CNN, RNN, LSTM, Sentiment Analysis, Deep Learning
Abstract: Sentiment analysis is a task of natural language processing that seeks to identify and produce the feelings or
viewpoints conveyed in written or spoken communication. It has various applications, such as social media
analysis, product reviews, customer service, chatbots, recommender systems, etc. In this paper, the author
evaluates the method on a large-scale dataset of fine foods reviews from Amazon, and compares it with several
models namely CNN, RNN and LSTM. The paper evaluates the outcomes of each model on three metrics:
Recall, Precision and F1 Score. The findings indicate that LSTM outperforms both TextCNN and RNN across
all metrics, making it the most effective model for this task. The paper also discusses the possible reasons for
the superiority of LSTM, such as its capacity to record context and long-term dependencies. The paper also
analyzes the advantages and disadvantages of TextCNN and RNN, such as their speed, simplicity, and
robustness. The paper provides empirical evidence for the effectiveness of different models for sentiment
analysis.
1 INTRODUCTION
Sentiment analysis is a research area that aims to
identify or generate the emotional attitude, mood or
tendency of natural language texts or speeches.
Sentiment analysis can be applied for many different
situations. For example, product reviews, chatbots,
recommender systems, etc. Sentiment analysis can
help users and businesses to understand the opinions,
preferences and feedbacks of customers or users, and
provide better products or services (Nandwani &
Verma 2021, Le-Khac et al. 2020, Sejwal et al. 2021).
Sentiment analysis is a challenging task, as it
involves various aspects of NLP (natural language
processing), such as syntactic analysis, lexical
analysis, semantic analysis, pragmatic analysis, etc.
Moreover, sentiment analysis is influenced by many
factors, such as the context, the domain, the culture,
the subjectivity, the sarcasm, the irony, etc.
Therefore, sentiment analysis requires not only the
understanding of the literal meaning of the texts or
speeches, but also the inference of the implicit
meaning and the emotional expression.
However, most of the existing deep learning
methods for sentiment analysis are embedded in
supervised learning, which requires a large amount of
labeled information for training and testing (Kohsasih
et al. 2022). The labeling process is often time-
consuming, labor-intensive, and subjective, and the
labeled data may not cover all the possible scenarios
and domains of sentiment analysis (Li et al. 2020,
Bordoloi & Biswas 2023). Moreover, the supervised
learning methods may suffer from the problems of
overfitting, data imbalance, domain adaptation, cross-
lingual transfer, etc (Zhao et al. 2021, Liu et al. 2020).
This paper proposes an unsupervised deep
learning method for sentiment analysis. It uses
contrastive learning with CNNs, RNNs, and LSTMs
to learn sentiment representations from texts or
speeches without labels. This method can handle
various sentiment analysis tasks, such as
classification, similarity, and generation.
To evaluate the method, the paper uses a large-
scale dataset of online foods comments from
Amazon, including more than 500,000 reviews.
2 METHODS
In this section, the paper describes the methods that
are used for sentiment analysis based on unsupervised
learning. The paper first introduces the contrastive
learning framework. Then, the paper describe the
encoder models we used to encode the texts or