Want to apply powerful natural language processing to your content marketing efforts?Doing some advanced entity based keyword research can be a great way to understand what Google expects to see in the results. And then create exactly that. But , you may ask what is sentiment analysis? What is text analysis? and the most important one: How can this help my business? Well, continue reading and you will find out all the information.
What is Sentiment analysis? It is a more advanced form of text analysis API. It is the interpretation and classification of emotions (positive, negative and neutral) in a text. Using SENTIMENT ANALYSIS allows you to identify customer sentiment towards products, brands or services by taling their online conversations and feedback. Instead of just looking for things like keywords topics, sentiment analysis goes a little deeper and is able to tell you exactly how users may feel towards a thing.
What is text analysis? A big portion of the internet is text content. Everything from instant messages, blogs comments, articles reddit posts , etc. Text analysis takes those texts and allows you to automatically extract and classify information from text content. Anything text based cab ne analyzed. Things like tweets, emails, support tickets, product reviews and survey responses and more to provide information like keywords, geographical information, statistics or sentiments.
However, before performing any kind of sentiment analysis, you will need to break down comments, paragraphs or documents, into smaller fragments of text. Customer feedback, for example, often contains multiple ideas or opinions, so analyzing the overall sentiment of reviews, tweets, documents, and so on, may result in a neutral classification. To get the most accurate results when mining for sentiment, you will need to use an opinion unit extractor, which separtes comments into individual opinions. It can be performed in jsut seconds on hundreds of pages and thousands of standalone opinions.
Here, it will be explained: First, different opinions or opinion units are extracted from the text. Next, you can organize these opinions into aspects categories. For the above example, based on a software review, the aspect classifier will tag our opinion units to fit into categories: Features, Ease of Use, Customer Support, etc. Once they separeted isnto aspects, they can perform sentiment analysis. In the end, each opinion unit is classified bi both topic and sentiment.
Explore Zyla to learn more. It is one of the most biggest marketplace of APIs that offers Sentiment Analysis API for text analysis. They have a specilized group that will be happy to guide you.
Zyla offers that you can get data on thousand of reviews in just minutes to find the most positive and most negative statements, find out what aspects of your business are most positive or most negative, extract the most important keywords and more. And the process can be chained together to work automatically with almost no need for human input.
Zyla Machine learning programs even allow you to train models to the language of your business and your own specific criteria. You simply feed training data into text analysis, programs, tag the samples to your criteria and machine learning algorithms learn how to porcess teh text to the training criteria you set up. For example, sentiment analysis with text mining, you would tag individual opinion units as “positive”, “negative” or “neutral” and the algorithms will learn how to extract and classify similar text features according to your training.
Text mining with sentiment analysis offers powerful data analysis insights and dynamic results, no matter the type of text you need to analyze. And once you train a sentiment analyzer to your specific needs, you can analyze your unstructured text at speeds and levels of accuracy you never thought possible.