A Comparative Study of Different Natural Language Processing Techniques for Sentiment Analysis and Opinion Mining

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@article{1709305,
      author = {Sujon Sarkar},
      title = {A Comparative Study of Different Natural Language Processing Techniques for Sentiment Analysis and Opinion Mining},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {12},
      pages = {1695-1708},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709305.pdf},
      abstract = {Sentiment analysis and opinion mining form an important area of research within Natural Language Processing (NLP) which aims to computationally identify, extract, and categorize subjective information expressed in a text. These technologies have played a decisive role in decoding human emotions, attitudes, and preferences within a huge range of applications, such as business intelligence, political forecasting, customer experience management, and social media monitoring. With the ever-increasing digital platforms and massive user-generated content-infused reviews, tweets, blogs, and comments-it has become a technical and strategic necessity to automate the understanding of sentiment in this information-rich environment. NLP technologies form the backbone of sentiment analysis systems, allowing the machine to parse, interpret, and reason into human languages. A sentiment analysis task relies on the effectiveness of the NLP methods deployed and their flexibility to the varying linguistic contexts and domains. In recent years, significant changes have been made in the area-from mostly lexicon-based and rule-based models to data-driven approaches, which incorporate both traditional machine learning classifiers and sophisticated deep learning architectures. They simply vary in areas like computational complexity, context capture, ambiguity resolution, and generalization across very different datasets. This research presents a comparative study of different NLP techniques used in the fields of sentiment analysis and opinion mining, with particular emphasis on comparing their performance in terms of classification accuracy, computational efficiency, and real-world validity. This comprehensive review evaluates a variety of techniques ranging from classical algorithms such as Na?ve Bayes and Support Vector Machines to their counterparts of neural models, namely Recurrent Neural Networks (RNN), Convolutional Neural Network (CNN), and the recent transformer-based architectures such as BERT and RoBERTa, all while trying to reveal the key performance dynamics and trade-offs associated with each method. The authors hope to establish a delicate nuance of model performance against different experimental conditions defined in terms of text length, degree of granularity of sentiment, specificity of domain, alongside linguistic complexities such as sarcasm, idioms, and code-switching. Still continue and even address those obstacles that were vital in this field of research like implicitness of sentiment, emotion sensitivity to context, and model limitations in terms of multilingualism or cross-domain settings. It points toward how the emerging trends in pre-trained language models and transfer learning initiated to change the scenario with good promise toward improving sentiment detection accuracy and less requirement of application for extensive labeled datasets. This research aims not only to direct efforts for the appropriate choice of NLP methods that would be most effective and applicable in different tasks of sentiment analysis; it will also serve as guidance for innovative frameworks toward building such intelligent and context-aware systems in opinion mining. This study further addresses some major issues that still pertain to sentiment analysis, such as the difficulty of implicit sentiment detection, context sensitivity, as well as the limitations of models across multilingual and cross-domain settings. It highlights emerging trends in pre-trained language models and transfer learning toward reshaping the landscape, promising to pave some pathways to improve sentiment detection accuracy with reduced needs for extensive labeled datasets. The findings are not merely aimed at informing the proper selection of effective and applicable NLP techniques in different sentiment analysis tasks, but they are intended to guide innovative frameworks, giving such intelligent and context-aware systems in opinion mining.},
      month = {June},
  }