Sentiment analysis is a subset of Natural Language Processing (NLP) that has huge impact in the world today. Essentially, sentiment analysis (or opinion mining) is the approach that identifies the emotional tone and attitude behind a body of text. So how does this come into play in our world? Since the internet has become an integral part of life, so has social media. When we search, post, and engage online—whether on social media or elsewhere—we can create influence or become influenced. This makes sentiment a potent weapon, as political campaigns, marketing campaigns, businesses, and prediction-based decision-making are all grounded in sentiment analysis.
For organizations to understand the sentiment and subjectivities of people, NLP techniques are applied, especially around semantics and word sense disambiguation. (Word sense disambiguation in NLP is the ability to determine the word's meaning in a particular context.) Social media often uses NLP techniques like speech tagging to understand sentence components such as subjects, verbs, and objects. This data is further analyzed to establish an underlying connection and to determine the sentiment’s tone, whether positive, neutral, or negative, through NLP-based sentiment analysis.
Data in the form of multimedia, text, and images are considered raw data. This raw data is utilized for NLP-based sentiment analysis. Different Machine Learning (ML) algorithms such as SVM (Support Vector Machines), Naive Bayes, and MaxEntropy use data classification. A primary tool used for the backend systems is word embedding. It is a representation of words in the form of vectors. Each word is linked to one 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, where each number is an attribute of the word's meaning. The semantically similar words with identical vectors, i.e., synonyms, will have equal or close vectors.
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 using unlabeled datasets and are allowed to act on that data without any supervision). The dataset used for algorithms operating around word embedding is a significant embodiment of text transformed into vector spaces. Some popular word embedding algorithms are Google's Word2Vec, Stanford's GloVe, or Facebook's FastText.
These are some of the challenges faced while using sentiment analysis:
This issue arises when data is not appropriately structured or has mismatching references.
It should be able to recognize and classify entities texts into pre-defined categories such as name, place, or other such other nouns.
It cannot separate sentences into subject or object 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 properly. It usually needs human input to help it understand.
Due to the casual nature of writing on social media, NLP tools sometimes provide inaccurate sentimental tones.
Sentiment analysis is not adept at understanding visual queues.
These challenges sow the way for improvements in sentiment analysis. Brand monitoring, customer service, and market research are at the level of regularly using text analytics. Moreover, sentiment analysis is set to revolutionize political science, sociology, psychology, flame detection, identifying child-suitability of videos, etc.
Organizations use sentiment analysis to predict a crisis, improve the experiences of unhappy customers, and even help run a marketing/political campaign. Manually, it is impossible to scan through all the posts or all the available texts on social media. Sentiment analysis helps convert unstructured text into structured data using NLP and open source tools.
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