Sentiment Analysis is becoming an essential element of market research, especially when it comes to engaging with customers or brand management. In this piece of article, we talk about the concept, the algorithms, the techniques, frameworks, and tools used for sentiment analysis. Above all, we also discuss briefly various applications of sentiment analysis and how businesses can benefit from it.
Sentiment Analysis is the process by which all the feelings (i.e. emotions, attitudes, opinions, thoughts, etc.) behind the words can be quantified by making use of Natural Language Processing (NLP) tools. If you are not aware of NLP tools, there’s nothing to worry. It’s just the application of computational techniques to the analysis and synthesis of natural language and speech.
Linguistic Revolution: The History of Writing and Reading a Language
Historian Yuval Noah Harari explains in his bestselling book ‘Sapiens – A Brief History of Humankind “Writing allowed humans to store ideas and thoughts outside of their brains.”
Thanks to the cognitive ability of humans brains – we have also developed the unique capability to read. Merely reading a post will let you identify whether the author had a positive stance or a negative stance on the topic – but that’s if you’re well versed in the language.
The Algorithm Behind Sentiment Analysis
We are now coming back to NLP. Unlike humans, a machine (in this case computer) has no concept of naturally spoken language – so, we need to break down this problem into mathematics (binary language – the expression understands). It cannot merely deduce whether something contains joy, frustration, anger, or otherwise – without any context of what those words mean. Sentiment Analysis solves this problem by using NLP. It recognizes the essential keywords and phrases within a document, which eventually help the algorithm to classify the emotional state of the text.
Keyword spotting is one of the simplest and most straightforward techniques and widely used by Sentiment Analysis algorithms. The input text is thoroughly scanned for the prominent positive and negative words like “sad”, “happy”, “disappoint”, “great”, “satisfied”, and such.
There are several Sentiment Analysis algorithms, and each has different libraries of words and phrases which they score as positive, negative, and neutral. These libraries are usually called the “bag of words” by many algorithms.
Consider the text,
“Ruined our vacation. We are never going here again.”
Humans can easily interpret that the text, as mentioned above, contains negative emotions.
Similarly, consider the text with positive emotions
“I am delighted with the work and the service. I’d visit again.”
Mathematically, the algorithm generates a score depending on the number of emotions (negative, positive or neutral) present in the statement.
Now consider the text “The food was horrible, but the ambience was awesome!” Now, this sentiment is more complicated than a basic algorithm can take into account – it contains both positive and negative emotions.
For such cases, more advanced algorithms were devised which break the sentence on encountering the word “but” (or any contrastive conjunction). So, the result becomes “The service was horrible” AND “But the ambience was awesome.”
This sentence will now generate two or more scores (depending on the number of emotions present in the statement). These individual scores are combined to find out the overall rating of a piece. In practice, this technique is known as Binary Sentiment Analysis.
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Sentiment Classification Techniques
Sentiment Classification techniques can be broadly divided into machine learning(ML) approach, lexicon-based approach and hybrid approach.
The Machine Learning Approach:
The ML approach applies the famous ML algorithms (both supervised and unsupervised) and uses linguistic features.
The Lexicon-based Approach: As the name suggests, it relies on a sentiment lexicon, the complete set of meaningful units in a language and pre-compiled sentiment terms. It is divided into the dictionary-based approach and corpus-based approach which uses statistical or semantic methods to find sentiment polarity.
The Hybrid Approach
The hybrid Approach combines both approaches and is very common with sentiment lexicons playing a vital role in the majority of methods.
Remember, no model is capable of achieving a perfect accuracy of 100%. Moreover, due to the complexity of our natural language, most of the sentiment analysis algorithms are only 80% accurate, at best.
Frameworks and Tools
Some of the popular libraries in Python
spaCy | Gensim | Pattern | NLTK | TextBlob | Polyglot | Vocabulary | PyNLPl | Stanford CoreNLP Python | MontyLingua | QuePy
Of course, these are not all the NLP libraries out there! If you look for NLP on PyPi, you will find more than 850 packages and libraries. Within the scope of this blog, it would be (almost) impossible to highlight all of these tools, libraries, and packages. TensorFlow and Keras are emerging categories of NLP libraries that are concerned with providing higher-level interfaces to various deep learning frameworks.
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Reputation Management – Also Known as Brand Monitoring
We all know the importance of a good reputation and online reviews. To buy stuff online, the majority of us check online reviews, including social media reviews, Google reviews, and other review platforms before making a decision. The same thing applies to research tools to use daily at work as a marketer.
Negative reviews put people off and how you handle can define your future as a business. You could either ignore them (highly not recommended). Act rude and make your situation even worse, or apologize for whatever caused a person to write a negative opinion and do your best to make up for it.
Facing Negative Reputation Online
However, you have to be aware of those opinions in the first place. That’s where social media monitoring, combined with sentiment analysis comes in!
Social media is a popular channel of communication with customers these days. Whenever a customer is unsatisfied with the service (whether or not it’s your fault), they prefer social media (Facebook/Twitter/Instagram). Customers expect brands to respond to social media almost immediately. If you’re not quick enough, you might see them moving on to your competitors instead of waiting for your reply. With the help of sentiment analysis, such mentions will appear in your dashboard, and you better start engaging them as soon as they are pop-up.
Chances are some of your competitors are getting bad press online. It’s where you could step in as long as you’re aware of those negative mentions. We don’t advise to take advantage of whatever they had neglected aggressively, but we highly recommend chiming in conversations when they don’t even bother to reply to the mentions they are getting.
At this stage, the implications are difficult to understate. More and more organizations are turning to sentiment analysis to understand consumer behaviors better. It’s gaining popularity and quickly building a reputation that is going to help propel it forward into the future towards more profound conclusions and insights.
It’s time to get on board with Esferasoft Artificial Intelligence Solutions and take advantage of these opportunities.