Social media platforms and blogs have drastically progressed both in number and in quality. The text data gathered from these apps about daily activities output interesting and relevant comments and reviews on several topics that are useful for organizations. People´s judgements help organizations to make decisions on the basis of opinion.
Nevertheless there are still some obstacles in achieving accurate interpretation of sentiments. Sentiment analysis spots and extracts personal insights by means of NLP (Natural Language Processing) and text mining. API developers are concerned about such obstacles and are constantly devising functionality to improve sentiment analysis and opinion mining with extraordinary results, even beyond expectations, to define future directions.
Researchers as well as businesses, governments and organizations in general have recently pumped up the use and prestige of Sentiment Analysis. It has been growing alongside the growth of Internet, as it has become the major source of worldwide information. Users and consumers use online applications to express their judgement regarding products and services.
It is a must to steadily monitor those reviews, and the solution to make that analysis automatically is the Opinion Mining or Analysis and Sentiment Mining or Analysis APIs. These platforms provide a structure for extracting reviews and identifying moods by means of the computational processing of unstructured data.
Zyla Labs Opinion Analysis API is essential for the detailed investigation, product analysis, and to find out which elements/components or features of a product are more appealing to customers. It allows to extract text features from reviews with noise or uncertainty, which gives awareness of the degree of satisfaction of a product or service in the market.
Zyla`s API has devised a strategy based on statistics and genetic algorithms to gauge the customer`s reviews and to categorize judgement. It has made up a lexicon that can be updated constantly to provide a more accurate classification of concepts from context. The range of activities that can make the most of this API goes from healthcare to airlines, from banks to hotels, from transport to any producer.
The information is to be found in tweets, posts, forums, surveys and all social media sites, blogs, reputation management, market research, competitor analysis, product analysis, customer voice, and so on and so forth. The sophistication of the functionality of Zyla`s API can even detect informal writing style (slang), language specific challenges, irony, and even sarcasm. It is not just about the aid of NLP and ML (Machine Learning), but also by means of transformer learning, hybrid approach and even lexicon-based strategies.
The API performs document as well as sentence level sentiment analysis; it analyzes phrase and aspect level sentiment; it collects data and selects features; it interprets punctuation, emojis, negations, and BoW (Bags of Words). The output is a quantitative report of positive, negative or neutral reviews, with the variables that each of them include. Polarity and indifference are also detected and reported, as they are relevant information for the client.
Zyla`s Opinion Analysis API classifies subjectivity hints, subjective ideas and emotional phrases. It classifies sentiment to determine the feeling in each extracted text. It detects Opinion Spam, generally known as fraud or phone reviews. It identifies implicit language that can be equivocal and ambiguous. It extracts aspect, which includes polarity classification and aggregation.
More accuracy, lower computational cost, more reliability and lower disbursement…to understand the comprehensive framework of sentiment analysis processing and the overall approach customization.