Sentiment Analysis challenges and ways to overcome them.
Tags: textanalysis, machinelearning, emotionanalysis, sentimentanalysis
Published: 2022-04-08 13:13:11


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A huge amount of data is being generated from forums, blogs, social sites, and other various platforms where people share their opinion. Gathering information manually about user-generated data is time-consuming, that’s why companies and organizations are opting for automatic [sentiment analysis](https://www.bytesview.com/blog/what-is-sentiment-analysis/) methods to help them understand it.

**What are the challenges in Sentiment Analysis and how to overcome them?**

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When it comes to challenges regarding [sentiment analysis](https://www.bytesview.com/sentiment-analysis), there are a few things that companies struggle with in order to obtain sentiment analysis accuracy. Sentiment analysis becomes difficult in natural language processing simply because the system has to be trained to understand, analyze and process emotions in the text as a human brain does. As data science is continuing to evolve, sentiment analysis softwares are becoming more and more able to tackle these issues better. Here are the main roadblocks in analyzing sentiment and how technologies/sentiment analysis APIs like **[Bytesview](https://www.bytesview.com/), monkeylearn, aylien** can be used to solve them.

**Challenge 1: Sarcasm**

People use sarcasm and irony in casual conversations on social media. This act of expressing negative sentiment using backhanded compliments allows sarcasm to easily cheat sentiment analysis models unless they’re specifically designed to take its possibility into account.

**solution**- A good sentiment analysis API like Bytesview or any other API will be able to detect the context of the language used in creating actual sentiment when something is posted. Bytesview is trained for 30+ language datasets on which the sentiment analysis model works which gives precise and accurate results.

**Challenge 2: Idioms**

Machine learning programs don’t understand a figure of speech usually. Idioms boggle the algorithm because it understands things in the literal sense. the sentence can be misconstrued by the algorithm or even ignored when an idiom is being used in a comment or review. The sentiment analysis platform needs to be trained in understanding idioms to overcome this problem. The problem becomes manifold when it comes to multiple languages.

**solution**- The only way this challenge can be solved with sentiment analysis accuracy is if the neural networks of the API are trained enough to understand and interpret idioms. Idioms are mapped according to nouns that denote emotions like joy, anger, success, determination, etc, and then the models are trained accordingly, only then can a tool for analyzing sentiment give accurate insights from such text.

**Challenge 3: Polarity**

Sometimes, a given sentence or document — or whatever unit of text we would like to analyze —exhibits multipolarity. In these cases, having only the total result of the analysis can be misleading, sometimes phrases get left out, which dilutes the sentiment score.

**solution**- A good sentiment analysis tool can easily figure out these words and mid-polar phrases in order to give an overall view of the comment. In this context, topic-based sentiment analysis can give a well-rounded analysis, but with aspect-based sentiment analysis, one can get an in-depth view of many aspects of a comment.

**Challenge 4: Comparative Sentences**

Comparative sentences are tricky because they may not always give an opinion. often it has to be deduced. For example, when somebody writes, “the laptop is lighter than the desktop”, here the sentence does not mention any negative or positive emotion but rather states a relative ordering in terms of the weight of the two entities.

**solution**- In this case, sentiment analysis accuracy can be achieved when a sentiment model compares the extent to which an entity has one property to a greater or lesser extent than another property, and then tie that to negative or positive sentiment. Training the AI machine to actually pull together information from its knowledge graph and analyze the relationship between entities, words, and emotions is the legitimate solution to this.

**Challenge 5: Multilingual Data**

Multilingual sentiment analysis constitutes all the problems one can think of in layman's terms and it gets compounded when a cocktail of languages is thrown in. Every language needs a unique part-of-speech tagger, lemmatizer, and grammatical constructs to understand negations.

**solution**- The sentiment analysis model needs to have a uniquely trained platform and named entity recognition model for each language like BytesView has. There is no shortcut to this because the model needs to be trained in each language manually. It is a time-consuming process that needs diligence and precision, but the results will give you the highest sentiment analysis accuracy scores possible.

**Wrapping up**

Every challenge we’ve covered can be easily tackled through the use of a strong sentiment analysis API. Bytesview’s software can analyze and report on customer sentiment, from comment tone to phrases with multipolarity to employee feedback and most of the things related. All of this is done through a wide range of AI-based techniques such as text analytics, natural language processing, and named entity recognition etc. **[Bytesview](https://www.bytesview.com/)** sentiment analysis platform understands multiple languages natively, which means wherever your business is, and whoever your customers are, you can get deep dives into consumer insights.