Sentiments analysis is a subset of Natural Language Processing (NLP). It is a data mining technique that measures and tries to understand people's opinions and stances through NLP. Computational linguistics and text analysis inspect information from the web, social media, and many other online sources. This data assesses people's emotions, sentiments, beliefs, or viewpoints, so it is also called opinion mining: essentially, companies utilize NLP to inspect people's moods in society. The internet has become an integral part of life. Therefore social media has become an essential platform of one’s life. Through participating in online platforms, one can create, express, influence, and even get influenced. The sentiment is a potent weapon in any arsenal: This is why Political campaigns, marketing campaigns, businesses, and prediction-based decision-making works on sentiment analysis.
For organizations to understand the sentiments of people, NLP techniques are applied, especially 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 companies often use NLP techniques like speech tagging and relationships to understand sentence components such as subjects, verbs, and objects. This data is further analyzed to establish an underlying connection, To determine the sentiment’s tone, positive or negative. Data in the form of multimedia, text, and images are considered raw data. This raw data is utilized for NLP-based sentiment analysis. These data sets can convey the tone or attitude of the text. These data sets are subjected to classification that involves syncing various independent classifiers, and then they are further classified.
Data in the form of multimedia, text, and images are considered raw data. This raw data is utilized for NLP-based sentiment analysis. It has been proven that ensemble classification is an improved version of traditional machine learning classifiers. 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, comedy properly. It usually needs a human to make 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 improvement while using 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 understand or reach the pivotal point of the matter, 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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