Sentiment Analysis, also known as Opinion Mining, refers to the techniques and processes that help organisations retrieve information about how their customer-base is reacting to a particular product or service.
In essence, Sentiment Analysis is the analysis of the feelings (i.e. emotions, attitudes, opinions, thoughts, etc.) behind the words by making use of Natural Language Processing (NLP) tools. If you’re not aware of what NLP tools do – it’s pretty much all in the name. Natural Language Processing essentially aims to understand and create a natural language by using essential tools and techniques.
Sentiment Analysis also uses Natural Language Processing and Machine Learning to help organisations look far beyond just the number of likes/shares/comments they get on an ad campaign, blog post, released product, or anything of that nature. In this article, we’ll be talking about Sentiment Analysis in great depth. From talking about the methods and tools of Sentiment Analysis to discussing why is it so extensively used – we’ve got it all covered!
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Table of Contents
Sentiment Analysis: The Math Behind It
Simply 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. However, a computer has no concept of naturally spoken language – so, we need to break down this problem into mathematics (the language of a computer). It cannot simply deduce whether something contains joy, frustration, anger, or otherwise – without any context of what those words mean.
Sentiment Analysis solves this problem by using Natural Language Processing. Basically, it recognizes the necessary keywords and phrases within a document, which eventually help the algorithm to classify the emotional state of the document.
Data Scientists and programmers write applications which feeds the documents into the algorithm and stores the results in a way which is useful for clients to use and understand.
Keyword spotting is one of the simplest technique and leveraged widely by Sentiment Analysis algorithms. The fed Input document is thoroughly scanned for the obvious positive and negative words like “sad”, “happy”, “disappoint”, “great”, “satisfied”, and such.
There are a number of Sentiment Analysis algorithms, and each has different libraries of words and phrases which they score as positive, negative, and neutral. These libraries are often called the “bag of words” by many algorithms.
Although this technique looks perfect on the surface, it has some definite shortcomings. Consider the text, “The service was horrible, but the ambiance was awesome!” Now, this sentiment is more complex 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 ambiance 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 consolidated to find out the overall score of a piece. In practice, this technique is known as Binary Sentiment Analysis.
No Machine Learning algorithm can achieve a perfect accuracy of 100%, and this is no different. Due to the complexity of our natural language, most of the sentiment analysis algorithms are only 80% accurate, at best.
Sentiment Analysis: Algorithms and Tools
The above graphic will give you a fair idea of the classification of Sentiment Analysis algorithms. Essentially, there are two types of Machine Learning algorithms:
You’re aware of the basic workings of any Machine Learning algorithms. The same route by followed in ML-based sentiment analysis algorithms as well. These algorithms require you to create a model by training the classifier with a set of example. This ideally means that you must gather a dataset with relevant examples for positive, neutral, and negative classes, extract these features from the examples and then train your algorithm based on these examples. These algorithms are essentially used for computing the polarity of a document,
As the name suggests, these techniques use dictionaries of words. Each word is annotated with its emotional polarity and sentiment strength. This dictionary is then matched with the document to calculate its overall polarity score of the document. These techniques usually give high precision but low recall.
There is no “best” choice out of the two, your choice of method should depend solely on the problem at hand. Lexical algorithms can achieve near-perfect results, but, they require using a lexicon – something that’s not always available in all the languages. On the other hand, ML-based algorithms also deliver good results, but, they require extensive training on labeled data.
Most Used Sentiment Analysis Tools
There are many Sentiment Analysis and tracking tools available for you to use. We’ll look at five such tools that find extensive use the industry today:
PeopleBrowsr helps you find all the mentions of your industry, brand, and competitors and analyse the sentiments. It allows you to compare the number of mentions your brand had before, during, and after any ad campaigns.
Meltwater is a social media listening tool that does everything from tracking impact and sentiment analysis in real-time to understanding the competitor’s footprints. Organisations like Sodexo, TataCliq, HCL, NIIT, and many others use Meltwater to improve their online presence and impact.
Google Analytics helps organisations discover which channels are influencing their subscribers and customers. It helps them create reports and annotation that keeps records of all the marketing campaigns and online behaviors.
The free version of HootSuite allows the organisations to manage and measure their presence on social networks. $5.99/month will make you a premium customer that’ll entitle you to use advanced analytics features.
Socialmention is a very useful tool that allows brands to track mentions for specific keywords in blogs, microblogs, videos, bookmarks, events, comments, news, hashtags, and even audios. It also indicates if mentions are positive, negative, or neutral.
Sentiment Analysis: Why should it be used?
With everything shifting online, Brands have started giving utmost importance to Sentiment Analysis. Honestly, it’s their only gateway to thoroughly understanding their customer-base, including their expectations from the brand. Social Media listening can help organisations from any domain understand the grievances and concerns of their customers – which eventually helps the organisations scale up their services. Sentiment Analysis helps brands tackle the exact problems or concerns of their customers.
According to some researchers, Sentiment Analysis of Twitter data can help in the prediction of stock market movements. Researchs show that news articles and social media can hugely influence the stock market. News with overall positive sentiment has been observed to relate to a large increase in price albeit for a short period of time. On the other hand, negative news is seen to be linked to a decrease in price – but with more prolonged effects.
Ideally, sentiment analysis can be put to use by any brand looking to:
- Target specific individuals to improve their services.
- Track customer sentiment and emotions over time.
- Determine which customer segment feels more strongly about your brand.
- Track the changes in user behavior corresponding to the changes in your product.
- Find out your key promoters and detractors.
Clearly, sentiment analysis gives an organisation the much-needed insights on their customers. Organisations can now adjust their marketing strategies depending on how the customers are responding to it. Sentiment Analysis also helps organisations measure the ROI of their marketing campaigns and improve their customer service. Since sentiment analysis gives the organisations a sneak peek into their customer’s emotions, they can be aware of any crisis that’s to come well in time – and manage it accordingly.
More or less every major brand these days relies heavily on social media listening to improve the overall customer experience. If you’re one of the interested souls and want to explore this topic in further depth, we recommend you go through the various kinds of algorithms (the ones we displayed in a graphic earlier) and implementations of Sentiment Analysis in more detail.
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What are the limitations of using automatic sentiment analysis?
Sentiment analysis is gaining popularity these days. However, subtleties such as irony, comedy, or sarcasm are difficult to determine with a simple sentiment analysis. Sentiment or emotion analysis may be challenging in natural language processing because machines must be educated to assess and comprehend emotions in the same way that the human brain does. Moreover, sentiment analysis of brief texts, such as single lines and Twitter posts, is difficult due to the lack of contextual information.
Which algorithm is preferred to be used for sentiment analysis?
For sentiment analysis, the XGBoost and Naive Bayes algorithms provide the highest accuracy. XGBoost is well-known for its speed as well as its great accuracy. The Naive Bayes method is well-known for its performance in various text classification tasks and requires less training data. As a result, using these two algorithms for sentiment analysis is highly preferred.
Is the use of LSTM preferred for sentiment analysis?
The LSTM network is a form of RNN network that can recognize long-term dependencies. They are frequently employed nowadays for a range of tasks such as speech recognition, text categorization, sentiment analysis, and so on. LSTM makes it simple to analyze sentiment in text reviews. LSTMs are specifically designed to overlook the problem of long-term reliance. It is basically their default habit to remember information for an extended period of time. As a result, it is preferred for sentiment analysis.