Implementing natural language processing techniques for sentiment analysis
By Udit Agarwal
Sentiment analysis, a subset of NLP, has a significant global impact. Essentially, sentiment analysis, also called opinion mining, is the approach that identifies the emotional tone and attitude behind a body of text.
Concerning this concept, have you ever wondered about the fact that how this comes into play in our world? The Internet has become an integral part of life. When we search, post over social media, and engage online, we can create influence or become influenced. This makes sentiment a potent weapon for decision-making.
How Do Organizations make utilization of Sentiment Analysis?
Word sense disambiguation in NLP is the ability to determine the exact meaning of a word in a particular context. Social media often utilizes NLP techniques, including speech tagging, to understand sentence components like subjects, verbs, and objects. This data is further analyzed to have the establishment of an underlying connection and also to determine the sentiment’s tone. It’s done via NLP-based sentiment analysis.
What is the workability of Sentiment Analysis?
Data comprising multimedia, text, and images are raw. This raw data is then further utilized by NLP-based sentiment analysis for analysis. Differentiated Machine Learning (ML) algorithms such as SVM (Support Vector Machines), Naive Bayes, and MaxEntropy utilize this data classification. Word embedding is the primary tool that is used for the backend systems. It is a representation of words in the form of vectors. Every word is linked with one vector in a vector, and the vector values are learned to look and work like an artificial neural network. Every word vector is then divided into a row of real numbers. The semantically similar words with identical vectors will have equal or close vectors.
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Word embedding is one of the most successful AI applications of unsupervised learning. Unsupervised learning is a type of machine learning in which models are trained by the better and more effective utilization of unlabeled datasets and allowed to act on that data without supervision. The dataset used for algorithms is a significant embodiment of text transformed into vector spaces. Some popular word embedding algorithms include Google’s Word2Vec, Stanford’s GloVe, and Facebook’s FastText.
Some of the challenges faced by Sentiment Analysis are:
This issue occurs in sentiment analysis with poorly structured data. This situation can also arise when the data has mismatching references.
Named Entity Recognition (NER)
The sentiment analysis data must be able to recognise and classify entity texts into pre-defined categories that include name, place, or other nouns.
Parsing is an issue that cannot separate sentences into subjects or objects and other parts of speech, such as adjectives, verbs, or pronouns. It needs to be more accurate.
Sentiment analysis does not have the skill to identify sarcasm, irony, or comedy correctly. It usually needs human input so that the data can be revealed.
Because of the casual nature of writing on social media, NLP tools sometimes facilitate inaccurate sentimental tones.
With the help of text analytics, brand monitoring, customer service, and market research are at the expected level. Moreover, sentiment analysis is set to revolutionize political science, sociology, psychology, flame detection, identifying the child-suitability of videos, and many more.
Organizations generally use sentiment analysis to predict a crisis, improvise the experiences of unhappy customers, and even help run a marketing/political campaign. You can manually scan either some posts or all texts on social media. Sentiment analysis helps the organization convert unstructured text into structured data by taking the help of NLP and open-source tools.