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ne-grained opinion information to form a summary representation in which expressions of opinions from the same source/target are grouped together, multiple opinions from a source toward the same target are accumulated into an aggregated opinion, and cumulative statistics are computed for each source/target. Yes, we re still learning. It s early days. If you re captivated by how human communication can be captured and deciphered, I suggest Opinion Mining and Sentiment Analysis by Bo Pang and Lillian Lee (www.cs. cornell.edu/home/llee/omsa/omsa-published.pdf). But isn t there enough technology out there somewhere that can pull all the bits and pieces together
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There have long been text analysis tools that focus on unstructured data like documents, spreadsheets, and survey results. Used primarily as search tools, they are now being trained on the ocean of emotion called the social media space. Analyzing the outpouring of millions of souls can reveal attitudinal shifts that are not visible to opinion polls, survey takers, or customer satisfaction questionnaires. Tracking public sentiment over time provides invaluable insight and gives you the chance to stay right on top of changes in the marketplace and your organization s brand equity. Social media speci c number crunchers are popping up all the time but as of this writing, they are immature curiosities rather than serious business tools.
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Chris Near, director of research at KDPaine & Partners, is no newcomer to measuring public opinion and the impact of PR campaigns. In a blog post in May 2009 (www.themeasurementstandard.com/issues/5-109/neartwittersentiment5-1-09.asp) he wrote, When my company, KDPaine & Partners, wants to do very accurate measurement of Twitter, we have to use human readers. It s the only way to really understand the language. Chris went on to describe ve different tools that, he said have their own set of problems. Their names have changed by now, the feature sets will have become richer, or they will have disappeared altogether. But the problems remain. In an ideal world, the ideal Twitter analysis tool would include:
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The ability to search by any word, abbreviation, symbol, or emoticon that would ever occur in a tweet. Advanced search features, including by date, by user, by hashtag, and by tone. The ability to distinguish between posts from a person/company, posts to a person/company, and posts referencing a person/company. Results charts with colors that distinguish between different sentiments. A dashboard with quick totals for each tone type, as well as overall numbers/percentages. The ability to search over long periods of time. Instant charts to show trends and relevant occurrences.
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At least 80 percent accuracy. 90 percent to 95 percent would be better. Real-time updates. No cost. (That s not too much to ask, is it ) Sadly, according to Chris, None of the tools reviewed . . . come even close to meeting this wish list. But some are on the right track. The main issue is that sentiment as it is commonly practiced today is based on the bag of words approach to parsing. If they include the words hate or bad or ridiculous, then it must be negative. The developers of Twittratr.com call it a StartupWeekend project. We built a list of positive keywords and a list of negative keywords. We search Twitter for a keyword and the results we get back are crossreferenced against our adjective lists, then displayed accordingly. There are obvious issues with this, so if you have any ideas on how we could do this better let us know. In this era of transparency, they posted their lists on Google Docs for all to review. Their lists are prosaic. As you can see from the examples taken from the positive and negative lists (Table 4.1), there are many combinations of words from both columns that might confuse and confound Twittratr. The bag-of-words approach is viable when sifting through petabytes of documents, trying to decipher what each one is about. Pure statistical engines compare and contrast the frequency of speci c words in a document against the frequency of those words in all other documents. But if your entire document is 140 characters, that method just doesn t have enough grist for the mill.
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