Sentiment Analysis is a process that is used to determine if a chunk of text is neutral, negative, or positive. In the process of text analytics, machine learning and natural language processing techniques are combined to assign sentiment scores to the topics, entities, or categories within a phrase.
How does Sentiment Analysis Work?
Opposite to generate sentence-based sentiment analysis systems, the Sentiment Analysis tool applies the sentiment to each topic in a single sentence. The internal process follows the below steps:
- First of all, a text is split into its primary components, such as sentences, entities, tokens, phrases.
- Then, each topic and related words are being identified
- Finally, in the end, a sentiment score is assigned to each and every topic, which includes the -1, +4, 0…
A combination of Natural Language Processing and Machine Learning for Sentiment Analysis
The major role of machine learning techniques in sentiment analysis is to simply automate the text analytics functions that sentiment analysis relies on the POS tagging, segmentation, and entity extraction. For instance, when the data scientists train any of the machine learning models by feeding it with a great number of text documents containing pre-tagged examples, it will help to automatically detect the sentiment analysis in future perspective documents. This is even possible thanks to both the supervised and unsupervised machine learning techniques such as deep learning and neural networks.
Different set of Machine learning models and algorithms also help data analysts to solve context-dependent problems which are being caused by the evolution of natural language processing (NLP). For instance, the adjective burned out may also bear some different meanings. Moreover, by simply considering the training methods as feeding machine learning models with lots of pre-tagged examples, the Machine learning systems can learn to understand what burned out means in the context of fire versus in the context of work-life.
Some of the hybrid sentiment analysis systems used to work together with the natural language processing and machine learning to reach higher accuracy. At this point in time, it is essential to make a difference between machine learning and natural language processing. While, a Natural Language processing-based sentiment analysis becomes an effective and efficient tool to build a foundation for sentiment analysis and POS based tagging. At the same time, machine learning techniques can help to solve some of the complicated natural language processing tasks, such as understanding double meanings via automated training.
A combination of Natural Language Processing and Machine learning techniques, thus, covers the entire text analytics process for the sentiment analysis, from the syntax analysis and low-level segmentation up to semantic set of differentiation which mainly depends on the context in which a word appears.
How Is Sentiment Analysis Used?
Sentiment analysis is mostly used as a tool for the voice of employees and the voice of customers with a different set of purposes and approaches. Companies can use different sort of sentiment analysis to get to know how employees and clients feel about some basic topics and to learn the major reason for those thoughts and opinions. These useful insights are then used to enhance the employee or client experience, which contributes to higher incomes and stronger productivity for the company:
- Sentiment Analysis for Customer Experience
In today’s modern digital scenario, most of your customers are using social channels, mainly Facebook,Instagram to talk and express their opinions about your product, brand, and services. Analyzing different tweets, news articles, and online reviews is very much useful for business analysts or social media managers to get some of the useful insights into how customers feel and even act upon it afterward. For this type of task, automated sentiment analysis plays a very significant component as a language processing tool.
- Sentiment Analysis for Employee Experience
A major percentage of the workers leave their jobs each year, while another portion is fired or let go. In this sense, Human resource teams are starting to take different actions, with the help of data analytics, to understand such tendencies and reduce the overall turnover while yielding a great performance improvement. Getting easily what employees are talking about and how they feel about your company is possible thanks to the sentiment analysis systems, and this helps the workforce analysts to reduce employee churn.