Here we are exploring the most complex natural language processing (NLP) issue: sentiment analysis challenges, and how to overcome them. Sentiment analysis has become an integral part of marketing. Not only can sentiment analysis accuracy help organizations establish how they are perceived, but it can also help them identify potential pitfalls in their marketing operations and branding content that can be dealt with on time. Though many companies face sentiment analysis challenges, these are not very difficult to overcome with the right solutions and collaboration partners. In this guide, we’ll break down some common challenges and all that’s needed to know to solve them.
What are the challenges in sentiment analysis?
When it comes to sentiment analysis challenges, there are quite a few things that companies struggle with in order to obtain sentiment analysis accuracy. Sentiment or emotion analysis can be difficult in natural language processing simply because machines have to be trained to analyze and understand emotions as a human brain does. This is in addition to understanding the nuances of different languages. As data science continues to evolve, sentiment analysis software is able to tackle these issues better. Here are the main roadblocks in analyzing sentiment.
Tone can be difficult to interpret verbally, and even more difficult to figure out in the written word. Things get even more complicated when one tries to analyze a massive volume of data that can contain both subjective and objective responses. Brands can face difficulties in finding subjective sentiments and properly analyzing them for their intended tone.
The basis of any good sentiment analysis software includes the ability to decipher subjective statements from objective ones and then find the right tone in it. For example: “The product is gorgeous but not at that price” is a subjective sentiment but with a tonality that says that the price makes the product less attractive. With a smart sentiment API, companies can decipher such nuances in tone, at scale.
Words such as “love” and “hate” are high on positive (+1) and negative (-1) scores in polarity. These are easy to understand. But there are in-between conjugations of words such as “not so bad” that can mean “average” and hence lie in mid-polarity (-75). Sometimes phrases like these get left out, which dilutes the sentiment score.
Sentiment analysis tools can easily figure out these mid-polar phrases and words in order to give a holistic view of a comment. In this context, a 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 within a comment.
People use irony and sarcasm in casual conversations and memes on social media. The act of expressing negative sentiment using backhanded compliments can make it difficult for sentiment analysis tools to detect the true context of what the response is actually implying. This can often result in a higher volume of “positive” feedback that is actually negative.
A top-tier sentiment analysis API will be able to detect the context of the language used and everything else involved in creating actual sentiment when a person posts something. For this, the language dataset on which the sentiment analysis model has been trained, needs to not only be precise but also massive.
The problem with social media content that is text-based, like Twitter, is that they are inundated with emojis. NLP tasks are trained to be language specific. While they can extract text from even images, emojis are a language in itself. Most emotion analysis solutions treat emojis like special characters that are removed from the data during the process of text mining. But doing so means that companies will not receive holistic insights from the data.
To meet sentiment analysis challenges like this, a company needs to employ an emotion analyzer tool that can decode the language in emojis and not club them with special characters like commas, spaces or full stops. This in itself is a very advanced application where models like Repustate’s are trained specifically for it. Data scientists first analyze whether people use emojis more frequently in positive or negative events, and then train the models to learn the correlation between words and different emojis.
Machine learning programs don’t necessarily understand a figure of speech. For example, an idiom like “not my cup of tea” will boggle the algorithm because it understands things in the literal sense. Hence, when an idiom is used in a comment or a review, the sentence can be misconstrued by the algorithm or even ignored. To overcome this problem a sentiment analysis platform needs to be trained in understanding idioms. When it comes to multiple languages, this problem becomes manifold.
The only way this challenge can be met with sentiment analysis accuracy is if the neural networks in an emotion mining API are trained to understand and interpret idioms. Idioms are mapped according to nouns that denote emotions like anger, joy, determination, success, etc. and then the models are trained accordingly. Suffice to say, only then can a tool for analyzing sentiment give accurate insights from such text.
Negations, given by words such as not, never, cannot, were not, etc. can confuse the ML model. For example, a machine algorithm needs to understand that a phrase that says, “I can’t not go to my class reunion”, means that the person intends to go to the class reunion.
A sentiment analysis platform has to be trained to understand that double negatives outweigh each other and turn a sentence into a positive. This can only be done when there is enough corpus to train the algorithm and it has the maximum number of negation words possible to make the optimum number of permutations and combinations.
Why do companies depend on sentiment analysis?
Companies depend on sentiment analysis to gain a deeper understanding of the consumer mindset. This translates into a better return on investment from more profitable marketing strategies. Sentiment analysis insights gathered from different sources lead to improved product features, pricing, store locations, customer experience, and overall employee satisfaction. Below are the main areas through which sentiment analysis helps businesses.
- Patient voice
- Social media listening
- Business intelligence
- Brand insights
- Reputation management
- Competitive analysis
- Opinion mining
- Voice of the Employee (VoE)
- Voice of the Customer (VoC)