By: Pinaki Sahu, International Center for AI and Cyber Security Research and Innovations (CCRI), Asia University, Taiwan, firstname.lastname@example.org
Sentiment analysis, a foundation that is rooted in the history of natural language processing(NLP) .This article delves into a comprehensive exploration of sentimental analysis, going through the importance of using sentimental analysis, a variety of applications and the journey to mastering sentiment analysis. We also delve into the essential tools and techniques while addressing the inherent challenges and proposing creative solutions. As we get to a conclusion, we not only reinforce the sentiment analysis’s ongoing importance but also point out some of its constantly increasing horizons, suggesting possible directions for further study and advancement.
Emotions are crucial for a healthy and successful human-to-human relationship. Uncovering hidden emotions and opinions within text becomes a fascinating endeavor, which can be achievable by delving into sentimental analysis. A fundamental component of Natural Language Processing (NLP), which empowers us to decode the emotional essence of written text. This beginner’s guide to sentiment analysis is your entryway to the fascinating world of comprehending human emotions through text.
Still Why Sentiment Analysis is Important?
Sentiment analysis is a key component of current analytics and processes for making decisions in today’s data-driven society, whether you are a business owner looking to measure customer satisfaction, a social media enthusiast tracking public opinion, or a data science novice looking to explore the wonders of NLP [8-12].
Here are some applications where sentiment analysis can be used:
- Customer Satisfaction and Feedback: Sentiment analysis has the ability to retain customer satisfaction. Businesses can acquire an in-depth understanding of what their customers think and feel about their goods and services by examining customer reviews, feedback, and comments.
- Marketing Strategies: Marketing techniques are greatly influenced by sentiment analysis. Businesses may instantly modify their marketing initiatives by tracking public opinion on their brand, products, or sector.
- Social media monitoring: Thoughts and opinions are frequently shared on social media platforms. Organisations can monitor social media conversations in real-time with the aid of sentiment analysis technologies. By quickly identifying and correcting rises in negative sentiment, it aids in crisis management by offering insights into brand reputation.
- Stock Market Analysis: In the financial industry, stock market analysis now includes sentiment analysis as a key component. Traders and investors use sentiment data to assess market sentiment, identify trends, and predict market movements.
- Customer Support and Service: Efficient customer support is paramount for businesses. Sentiment analysis can categorize and prioritize customer inquiries and complaints, ensuring that urgent issues receive immediate attention.
- News and Media: Media organizations use sentiment analysis to track public sentiment regarding news topics and stories. This helps in editorial decision-making and content curation. Media streaming platforms use sentiment analysis to recommend content based on a user’s emotional preferences.
- Healthcare and Patient Feedback: Patient Experience Analysis: Sentiment analysis is used in healthcare to analyse patient feedback and reviews. Hospitals and healthcare providers use this data to improve patient experiences and address areas of concern.
How to Start Learning Sentiment Analysis?
Since it is a powerful tool, sentiment analysis might be confusing to those new to natural language processing (NLP). However, anyone may start their journey to mastering sentiment analysis with a disciplined strategy and the appropriate tools. We present a step-by-step roadmap for beginners to dig into sentiment analysis in this thorough tutorial, covering everything from basic NLP ideas to helpful advice for choosing datasets and tools.
- Understand the Basics of Natural Language Processing (NLP):Learning NLP’s foundational concepts is essential before diving into sentiment analysis. Artificial intelligence’s NLP focuses on how computers and human language interact. Start by learning about key NLP concepts such as tokenization (breaking text into words), stemming, and part-of-speech tagging (identifying the grammatical category of words).
- Learn Text Preprocessing: Text data must be clean and well-processed for accurate sentiment analysis. The tasks involved in text preparation are: Removing punctuation and special characters. Lowercase text conversion for uniformity. Stop words (common words like “the,” “and,” and “is”) should be eliminated. Tokenization and lemmatization(the reduction of words to their dictionary or simplest form). The information you provide will be ready for sentiment analysis if you understand and apply these preparation methods.
- Become Familiar with Sentence Lexicons: Lexicon-based sentiment analysis uses dictionaries that pair words with different sentiment ratings (such as positive, negative, and neutral). Study well-known sentiment lexicons first, such as the VADER. These lexicons offer a list of words and phrases together with the sentiment scores that go with them. Learning how to use these lexicons to analyse text sentiment is a foundational skill in sentiment analysis.
- Study strategies based on machine learning: Sentiment analysis powered by machine learning is more sophisticated but also very precise. It entails developing models to forecast sentiment using labelled data. Learn about the Naive Bayes, Support Vector Machines (SVM), and more sophisticated deep learning models like Long Short-Term Memory (LSTM) networks and BERT, which are machine learning techniques frequently used for sentiment analysis.
- Gather Labelled Datasets: To practice sentiment analysis, you’ll need labelled datasets, which are texts that are already tagged with their sentiment (e.g., positive, negative, neutral). There are several publicly available sentiment analysis datasets, such as the IMDb movie review dataset or the Twitter sentiment dataset. Choose a dataset that aligns with your interests or the industry you’re targeting for your analysis.
- The Best Tools and Libraries to Use: Using the correct tools and libraries is critical for effective sentiment analysis. A number of popular NLP libraries, like NLTK (Natural Language Toolkit) and spaCy, include text preparation and analysis capabilities. Python-based libraries such as VADER make sentiment analysis easier for beginners. TensorFlow and scikit-learn are powerful machine learning-based sentiment analysis tools.
- Experiment, Practise, and fine-Tune: The key to becoming an expert in sentiment analysis is practise. Begin with a brief lexicon-based assessment to gain confidence. Then, go on to machine learning-based approaches and experiment with different features and algorithms. Investigate research articles, online classes, and tutorials to keep your models and procedures up to date.
- Create Real-World projects: After gaining experience, think about using sentiment analysis on real-world projects. To demonstrate your abilities and earn real experience, analyse news stories, consumer reviews, or social media data.
The Challenges you’ll encounter with Sentiment Analysis
Despite the fact that sentiment analysis is a powerful tool for analysing the emotions and ideas expressed in texts, sentiment analysts still face a number of challenging obstacles. we delve into these difficulties, breaking down the complications related to context-sensitivity, sarcasm and irony, data quality problems, giving readers a thorough knowledge of the difficulties faced by sentiment analysts.
- Irony and Sarcasm: Irony and sarcasm frequently use language that seems positive at first look but actually conveys a negative emotion. For instance, the phrase “Oh, great, another Monday” may appear positive because of the word “great,” yet the true meaning is one of disapproval.
- Context Sensitivity: Depending on the situation, words can convey a variety of emotions. For instance, depending on the context, “sick” might refer to being ill or extraordinary.
- Data Quality Issues: Text data often contains noise, irrelevant information, or off-topic content, which can affect sentiment analysis accuracy. Understanding and correctly grouping words can be difficult when using informal language, misspellings, and slang.
- Domain-Specific Sentiment: Different sectors or domains may see different results from sentiment analysis models. Expressions of emotion, for instance, may vary greatly between entertainment and the healthcare industry.
- Emojis and Emoticons: Emojis and emoticons are being used more and more to describe emotions, however, their meaning might vary depending on the situation and the culture.
Solution to Challenges in Sentiment Analysis
Innovative approaches and advanced techniques are needed to overcome the complexity of sentiment analysis in order to meet the many difficulties found in the field. In this thorough investigation, we go into specific remedies for the aforementioned problems, offering workable strategies to improve the precision and efficacy of sentiment analysis.
- Irony and Sarcasm: Develop models that recognise the emotional basis of sarcastic or ironic statements, which may combine humour and criticism.
- Context Awareness: Use contextual analysis methods that take into account the larger context in which a word or phrase is used. Context understanding is a strength of bidirectional models like BERT.
- Data quality issues: Implement reliable data preprocessing processes to remove noise, fix misspellings, and manage slang.
- Domain-Specific Sentiment: To adapt sentiment analysis to particular industries, train sentiment analysis models on domain-specific datasets.
- Emojis and Emoticons: Develop complete emoji dictionaries that relate emojis to emotional scores while taking context and cultural differences into consideration.
This article outlines the steps to mastering sentiment analysis by highlighting key tools and methodologies. It recognises difficulties such as sarcasm, context, and emoticons and offers new solutions such as powerful machine learning and data preprocessing.
In the future, sentiment analysis will continue to evolve. Future research will focus on improving multilingual analysis, combining sentiment analysis with other NLP tasks, and investigating contextual understanding.
Essentially, sentiment analysis is a dynamic asset in our data-driven world, allowing us to identify sentiments in text, influence decision-making, and improve our knowledge of human expression. It is still a fascinating and important subject within NLP, ready for further research and application.
- Cambria, E., Das, D., Bandyopadhyay, S., & Feraco, A. (Eds.). (2017). A practical guide to sentiment analysis (Vol. 5). Cham: Springer International Publishing.
- Yue, L., Chen, W., Li, X., Zuo, W., & Yin, M. (2019). A survey of sentiment analysis in social media. Knowledge and Information Systems, 60, 617-663.
- Joshi, A., Bhattacharyya, P., & Ahire, S. (2017). Sentiment resources: Lexicons and datasets. A Practical Guide to Sentiment Analysis, 85-106.
- Ahmet, A., & Abdullah, T. (2020). Recent trends and advances in deep learning-based sentiment analysis. Deep learning-based approaches for sentiment analysis, 33-56.
- Gupta, S., Singh, R., & Singla, V. (2020). Emoticon and text sarcasm detection in sentiment analysis. In First International Conference on Sustainable Technologies for Computational Intelligence: Proceedings of ICTSCI 2019 (pp. 1-10). Springer Singapore.
- M. S. Razali, A. A. Halin, N. M. Norowi and S. C. Doraisamy, “The importance of multimodality in sarcasm detection for sentiment analysis,” 2017 IEEE 15th Student Conference on Research and Development (SCOReD), Wilayah Persekutuan Putrajaya, Malaysia, 2017, pp. 56-60, doi: 10.1109/SCORED.2017.8305421.
- Labille, K., Gauch, S., & Alfarhood, S. (2017, August). Creating domain-specific sentiment lexicons via text mining. In Proc. Workshop Issues Sentiment Discovery Opinion Mining (WISDOM) (pp. 1-8).
- Al-Smadi, M., Qawasmeh, O., Al-Ayyoub, M., Jararweh, Y., & Gupta, B. (2018). Deep Recurrent neural network vs. support vector machine for aspect-based sentiment analysis of Arabic hotels’ reviews. Journal of computational science, 27, 386-393.
- Gunti, P., Gupta, B. B., & Benkhelifa, E. (2022). Data mining approaches for sentiment analysis in online social networks (OSNs). In Data mining approaches for big data and sentiment analysis in social media (pp. 116-141). IGI Global.
- Sethi, A., Chui, K. T., Gupta, B. B., Arya, V., Castiglione, A., & Zhang, J. (2023, January). Low Resource Vs High Resource solutions for Federated learning sentiment analysis. In 2023 IEEE International Conference on Consumer Electronics (ICCE) (pp. 1-3). IEEE.
- Singh, S. K., & Sachan, M. K. (2021). Classification of code-mixed bilingual phonetic text using sentiment analysis. International Journal on Semantic Web and Information Systems (IJSWIS), 17(2), 59-78.
- Salhi, D. E., Tari, A., & Kechadi, M. T. (2021). Using e-reputation for sentiment analysis: Twitter as a case study. International Journal of Cloud Applications and Computing (IJCAC), 11(2), 32-47.
Sahu P. (2023), Simplifying sentiment analysis: A Beginner’s Guide, Insights2techinfo, pp.1