Tools · Twitter

Opinion Crawl Tracks and Semantically Analyzes Sentiment on Twitter and News

I have reviewed several tools to analyze sentiment: their methods varied from manual sentiment setting to textual analysis, emoticons and symbols. Today we are looking at a semantic sentiment analyzer: Opinion Crawl which uses SenseBot (we have mentioned previously) as the semantic analysis engine.

Opinion crawl has two large section we’ll review separately: the sentiment tracker (for hot topics news broken into categories) and sentiment search.

Opinion Crawl Sentiment Tracker

The main page displays key topics grouped into categories:

  • Current events (Oil spill, Afghan war),
  • Political figures (Barack Obama),
  • Entertainment (Lady Gaga, The Last Airbender),
  • Companies (Goldman Sachs),
  • Economy (Unemployment, Chinese economy),
  • Products (iPad), etc.

Clicking on a topic takes you to the blog which is automatically generated every day. The blog tracks the sentiment trend on a large number of Web publications and provides a daily/weekly/monthly view. For example, clicking on “The Twilight Saga” (it’s not that I am a fan, it’s just that I found that example most interesting) takes us to this page that visualizes:

1. Daily Sentiment:

opinion crawl 01 Opinion Crawl Tracks and Semantically Analyzes Sentiment on Twitter and News

2. Sentiment Trend

(Trending of positive/negative/neutral and overall mentions over a period of time):

opinion crawl 02 Opinion Crawl Tracks and Semantically Analyzes Sentiment on Twitter and News

3. Positive-to-Negative Ratio

(Relation of positive mentions to the negative ones)

opinion crawl 03 Opinion Crawl Tracks and Semantically Analyzes Sentiment on Twitter and News

Opinion Crawl Search

There are 2 search buttons on the main page: Sentiment in the news and Sentiment on Twitter. They allow you to get an ad-hoc reading of the sentiment on a small number of recent news items or tweets. (Note that the ad-hoc analysis presumes that the topic you are searching for is in the news or is being actively tweeted about.)

The result is displayed as a snapshot of the current sentiment as a pie-chart; latest news and images on the topic.

opinion crawl 04 Opinion Crawl Tracks and Semantically Analyzes Sentiment on Twitter and News

One of the most useful features I loved was a semantic cloud consisting of key concepts extracted from the news or tweets. The concepts allow you to see which issues may be driving the sentiment in a positive or negative direction.

opinion crawl 05 Opinion Crawl Tracks and Semantically Analyzes Sentiment on Twitter and News

Analysis of Twitter consists of searching the tweets on a topic and analyzing their text. Many tweets do not have enough textual content – they may just contain a URL pointing somewhere and a couple of words. So the engine needs to extract a decent volume of tweets and parse them for sentiment expressions, and for key concepts that are being discussed (semantics).

Twitter sentiment often swings widely throughout the day, whereas the news sentiment is more stable.

Compare the Twitter sentiment for “Old Spice”, especially the sematic cloud with the above screenshots – amazing!

opinion crawl 06 Opinion Crawl Tracks and Semantically Analyzes Sentiment on Twitter and News

A pleasant surprise was that the tool was quite reliable at identifying the current sentiment, you may want to give it a try and let me know your thoughts!

 Opinion Crawl Tracks and Semantically Analyzes Sentiment on Twitter and News
Ann Smarty is the blogger and community manager at Internet Marketing Ninjas. Ann's expertise in blogging and tools serve as a base for her writing, tutorials and her guest blogging project, MyBlogGuest.com.
 Opinion Crawl Tracks and Semantically Analyzes Sentiment on Twitter and News

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3 thoughts on “Opinion Crawl Tracks and Semantically Analyzes Sentiment on Twitter and News

  1. This is a good way to make the site more organized and making the people know the status of the people’s opinion if it is really good or bad. I just hope that all sites could have like this to make their viewers know about the people choice or likes.