man analyzing the sentiment of an article

Sentiment Analysis

What is Sentiment Analysis?

Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral. It’s also known as opinion mining, deriving the opinion or attitude of a speaker. A common use case for this technology is to discover how people feel about a particular topic.

Say you want to know if people on Twitter think that Chinese food in San Francisco is good or bad.

Twitter sentiment analysis will answer this question. You can even learn why people think the food is good or bad, by extracting the exact words that indicate why people did or didn't like the food. If too salty shows as a common theme, for example, you immediately have a better idea of why consumers aren’t happy.

This is the kind of insight one aims to find through market research, but why devote enormous budgets and countless man-hours to conducting surveys and cold calling? Through Lexalytics text mining tools, you’ll get answers in seconds from the comfort of your office chair.

Below you will find a high-level overview of intentions and Lexalytics’ sentiment analysis tools. For a more in-depth explanation of our sentiment analysis, read through “How Lexalytics Does Sentiment Analysis”. Check out our web demo to see Lexalytics in action, or get in touch to schedule a live demo.

Multi-level Analysis

Lexalytics’ sentiment analysis tools can be configured to determine sentiment on a range of levels. We’ll score sentiment on a document level (does this express a general positive or negative tone), but we’ll also score the sentiment of individual words or phrases in the document.

Less precise sentiment analysis tools lump together the sentiment expressed at individual entities into a general document sentiment score. Given a Facebook comment that reads:

I love the summer in New York, but I hate the winter.
A weak sentiment analysis system will score love the summer as positive and hate the winter as negative, but will report the entire comment’s sentiment as neutral (the positive love and the negative hate cancelling each other out). Lexalytics recognize the importance of that middle word, but, and so we report separate sentiment for the first and second parts of the sentence.

sentiment phrases
In order to determine the sentiment of the overall document, we can use our own scoring algorithms – using the weighted phrases from the previous side, and then using our proprietary way of adding them up. We can also take a set of sentiment tagged content and build a document-level sentiment classifier.
document sentiment
First identify the sentiment phrases (and not, say, a proper noun like “Good Morning America”), apply things like “intensification” and “negation” – for stuff like “good” “very good” “not very good”, etc. Then, in English, use the syntax matrix to determine the syntactic effect of the ordering of the words.

Lexalytics provides sentiment analysis solutions directly to businesses, as well as offering APIs for integration into our client’s own products. Hundreds of companies around the world rely on Lexalytics sentiment analysis tools to track and monitor public opinion of their products, services, or organization in general. If someone is attacking your brand on social media, our sentiment analysis systems score the relevant posts as extremely negative, and a social media monitoring solution flags them for immediate response.

Salience and Semantria process billions of documents every day in a wide range of industries, from Hospitality to Financial Services to Customer Experience Management and beyond. Our text analytics solutions drive better business decisions for scores of companies of all sizes around the world.

Lexalytics sentiment analysis is the most accurate and reliable in the market. We’ve spent over a decade refining our systems so that you, the busy professional, can sit back and let our products save you time, money, and headache.

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