How many of you go through reviews before purchasing a product or a service on any e-commerce marketplace? I bet, many of you. But, how many reviews do you read? Five, ten or at maximum twenty. Ever thought of a system that can analyze the reviews for us and save us from the painful process of going through the reviews manually and that too in few seconds. And since the system has considerable computational capabilities, it just does not restrict itself to five or ten reviews but tens of thousands of reviews at once. The above process of analyzing is made possible using sentiment analysis and just one use case out of many use cases of sentiment analysis.
According to Wikipedia, “Sentiment analysis refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.”
In today’s world, it is quintessential for companies to know what their customers are talking about their products online, and sentiment analysis helps in the same, that is, to gauge the perception of customers or the general public towards a product or a service.
Sentiment analysis are mainly polarity based, aspect-based or emotion detection-based. How polarity-based sentiment analysis works is, it focusses on the text and classifies the words into positive, negative and neutral terms. The higher the number of types of terms, the text is categorized into that polarity. Emotion detection, as the name indicates, detect the emotion like happiness, sadness, and anger based on the text. Whereas, aspect-based sentiment analysis determines the aspects or features of the products that the customers are interested in.
Like stock or commodity markets, it is believed that news and social media tend to affect the cryptocurrency trading as well. And based on the sentiments of the unprecedented news, there may be price fluctuations in the crypto-market. This has led to an increase in usage of sentiment analysis to gauge a significant relationship between the news and the price of cryptocurrencies. In the past few years, many such instances lead us to believe that the theory holds. One such instance comes from November 2017; crypto markets, specifically Bitcoin saw a drastic surge in the prices which got fuelled from positive reviews from the business houses and an increase in retail investors. Further, after one month, Bitcoin faced a great slump in their history as a result of attacks from government authorities, skeptical influencers, and other business communities.
Crypto markets are unregulated, unlike the stock, commodity or currency markets. As a result, any news, positive or negative, regarding the markets tend to influence it substantially. Moreover, unlike broader markets, cryptocurrencies are decentralized networks and are managed by a diverse set of retail investors spread across the world. Therefore, it is believed that sentiment analysis in the field of cryptocurrencies can help in gauging public sentiment.
However, there are certain limitations in using sentiment analysis for cryptocurrencies markets. One of the limitations is the inapplicability of general NLP (Natural Language Processing) technologies in domain-specific problems like cryptocurrency market analysis. In a general API, we need to train the model in such a way that they recognize the domain-specific terminologies used to implement sentiment analysis. Further, sentiment analysis itself has drawbacks that limit its usage in the crypto markets as these markets are still in a nascent stage. It is difficult to analyze the sentiments with NLP techniques, which form the backbone of sentiment analysis if the text contains ironies, bad grammar, sarcasm, etc. For an example, a response to ‘Did you like our services pleasant?’ can be ‘I could not agree more’, this response can be taken as negative by the model as it contains terms ‘not’ and ‘agree’ together. Similarly, there are multiple instances like this where NLP techniques will not be able to deliver results up to the mark.
Sentiment analysis has already made inroads in the crypto markets. However, the implementation of domain-specific NLP models and rigorous machine learning algorithms can make it effective in the cryptocurrency market space. As the cryptocurrency markets evolve like the broader ones, we can expect sentiment analysis to gauge the public sentiments more conclusively and predict the fluctuations of the market related to it.
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About the Author:Abhishek Kumar is pursuing MBA in Finance from IIM Lucknow. A computer science graduate who has worked in Fintech industry, he is always keen to learn about developments in financial sector, technology and innovation.His interests include eclectic reading, and technical and experiential writing.”
