Problems Mining Sentiment Analysis Data
Jubilation, happiness, anger, joy, rejoice, discontent, rage, or delight. You can categorize it for simply being positive or negative sentiment or you can weigh the emotions behind each word to find out the exact attitude towards your brand. People are talking about your brand extensively on social media or by giving away direct feedbacks on […]
Jubilation, happiness, anger, joy, rejoice, discontent, rage, or delight. You can categorize it for simply being positive or negative sentiment or you can weigh the emotions behind each word to find out the exact attitude towards your brand. People are talking about your brand extensively on social media or by giving away direct feedbacks on emails or calls. If a company aspires to become the best and be customer oriented, they must actively listen to what their customer has to say and what they feel about their brand and the company. Sentiment analysis is not only used by businesses but also in decrypting public emotions in relation to a political campaign. It was extensively used during the US presidential elections to figure out what people thought about each candidate. Although sentiment analysis seems to be an invaluable tool to understand customer emotions, using it is not as easy as it may seem.
Challenges with sentiment analysis
Although it may seem like the English language is straightforward and follows a set rule such as subject-verb-object pattern, it is complicated by the use of double negatives and multiple opinions in a statement. For instance, ‘all feedbacks for this place seems awfully bad’ is completely opposite to ‘I don’t like reading all the negative comments’. Additionally, sarcasm may also signal false positives. Many cases automated response in social media failed as they failed to identify sarcastic comments. For instance, bots may think that ‘Air Asia is such a great airline. You never know where you’ll end up’ is a positive emotion.
Grammar and other mistakes
Let’s face it! An average customer doesn’t give his feedback with the same seriousness as he would write a college paper. Grammatical mistakes, spelling errors, and the use of shorthand are unavoidable. Sentiment analysis tools don’t work as it may not be able to map the words to correct emotions or sometimes fail to identify the word itself. Also, algorithms and programs may not be able to decipher abbreviations. Abbreviations like ty, tysm, and grt may not be understood by the system and thereby would not be able to make a correct analysis.
Fake opinions and review
Fake and bogus reviews can misguide the readers and customers by providing them with untruthful positive or negative opinions related to any company or product. This may be done to boost or lower the reputation of the company. Such fake opinions make sentiment analysis useless in various application areas as they aren’t the true voices of the consumers.
Real-time opinion mining
Opinions are a subjective matter and can change frequently with time. People use social media to express their opinion, which is why companies are progressively looking for ways to mine social media data and information on what people think and experience about a particular product. One of the biggest challenges in implementing sentiment analysis is to collect and analyze data in a real-time environment, which can help the brand with disaster management and instant feedback incorporation.
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