The article talks about Sentiment Analysis, categories of sentiments, types of Sentiment Analysis techniques, and limitations of the process.
Sentiment analysis, also known as opinion mining, is an automatic massive document classification task that focuses on cataloging documents based on the positive or negative connotation of the language used in it. Sentiment analysis involves using natural language processing, text analysis, and statistics to analyze consumers’ sentiments.
What is the Use of Sentiment Analysis?
Businesses these days are very much concerned about their online reputation. They are digging data intensively to understand how the customers feel towards their brand, what they like, what they do not, or even product/services feedbacks. The most popular platforms, like Twitter, Facebook, Quora, etc., receive millions of brand mentions that need to be analyzed and understood.
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Ability to correctly interpret complex constructions such as –
|“McDonald’s is much better than Burger King”||Positive for McD and negative for BK|
|“I did not like the eighth season of Game of Thrones“||Negative|
|“I would buy an iPhone if it wasn’t so expensive”||Conditional|
Sentiment analysis, in general, aims to deal with all the peculiarities of natural speech, starting from the use of direct sentences like above to the use of sarcastic and unclear sentences.
Brands following good sentiment analysis methodologies are able to draw conclusions and make concrete decisions based on the information they provide. They can plan their strategies and take corrective actions basis the information received from sentiment analysis.
Types of User Sentiments
When talking about sentiment analysis is significant to categorize the types of sentiments. There are many possibilities to categorize the feeling of a text, and each tool or work team can use a different one. Hence the importance of choosing the right one for you.
Polarized – This is the most common analysis and consists of polarizing an opinion towards something positive, something negative or something neutral. In some cases, it is chosen to make it more granular, including the categories of ‘very positive’ and ‘very negative’.
Intention – Intention is a classification that focuses on what users want to do with what they say and exactly what they say. Such types of responses usually mean – interested or not interested.
Emotional – To classify emotions, you need a very specialized classification technique. Common emotions – Joy, Happiness, Frustration, Anger, and Sadness form the base of product launches or marketing campaigns. Emotions help measure how aligned the expectations from a product are against audience opinion.
There are many more variants when talking about a person’s feeling and how it is transmitted in a text. However, these are the most common and exemplify the complexity involved in classifying the sentiment of a text.
Elements of Sentiments Analysis
The fundamental elements of sentiment analysis are –
Monitoring engine or crawler – A monitoring engine or crawler finds the texts parsed. These tools usually incorporate search capabilities in social networks and on the web to find all the mentions of the keywords that we have defined and pass them to the analysis engine for evaluation.
Information visualization and exploitation tool – An information visualization and exploitation tool collects the information provided by the sentiment analysis engine and composes the user interface with it. It also aggregates the information on sentiment with other available relevant data.
Classification of Sentiment Analysis
We can classify the Sentiment Analysis process into two main categories –
Machine Learning Based Approach – ML-based approach uses machine-learning techniques and algorithms, which are trained to create text identifiers and classifiers that predict the accurate sentiment embedded in the text. The recent development in this field is the use of deep learning methods, which are well suited for data learning representations.
Rule or Lexicon-Based approach – Rule-based methodology uses words annotated by a polarity score. This helps to give a score to the received content. This method is very independent of any training data, while one point to consider is that the sentiment lexicon often lacks a considerable number of words and expressions.
Another approach getting popular these days is –
Hybrid Approach – The hybrid approach is a combination of machine learning and lexicon-based approaches and often produces results that are more promising.
Best Algorithms for Sentiments Analysis
Artificial intelligence and machine learning based algorithms are widely used to perform sentiment analysis. Let’s take a look at the most popular algorithms –
- Naive Bayes
- Support Vector Machine
- Decision Tree
- k-Nearest Neighbors
- Naive Bayes – Support Vector Machines (NBSVM)
- NLTK word-tokenizer
- Stanford’s CoreNLP sentiment analyzer
- Recurrent Neural Networks
- Long Short-Term Memory (LSTM)
- Recursive Neural Tensor Network
- Convolutional Neural Networks
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What are the Limitations of Sentiment Analysis?
There is precisely no method of correctly combining the different words for the sentiment analysis to be 100% reliable. Systems limited to keyword content extraction and configuration cannot generate satisfactory sentiment analysis results in their entirety. The complexity of the human language gives this. For example, how do you instill in a robot the ability to define whether a phrase is made sarcastically or not?
Many algorithms make mistakes, finding it impossible to set the exact length of the comment or the real intention of a particular word. That is, they cannot infer from an exact assessment of the different semantic relationships, and it can be said that it is currently impossible to achieve 100% success in this field.
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