"Sentiment Prediction through Text Mining"

Braulio

Hi there!

We would like to start building a sentiment predicting system. That system should be able to predict market humor about companies/products.
I know this is a complex task and I would like to get some tips about building such a system using Rapid Miner.

Would Text Classification with Cross Validation be suitable to separate good from bad news about a company X?

Do you recommend any books in this field?

Thank you very much and hope to get things going soon.

Braulio

TobiasMalbrecht

Hi Braulio,

We would like to start building a sentiment predicting system. That system should be able to predict market humor about companies/products.
I know this is a complex task and I would like to get some tips about building such a system using Rapid Miner.
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Well, building such a system is certainly possible with RapidMiner. In fact, RapidMiner is a very suitable tool for such tasks, as despite its wide coverage of data mining / bi / etl approaches it is highly intuitive and can therefore be excellently used for what we call Rapid Prototyping which means to set up initial data mining processes easily and in a relatively short time. Due to the fact that RapidMiner can also be easily integrated into your applications, you can build a full system around the processes you just set up.

Braulio wrote:
Would Text Classification with Cross Validation be suitable to separate good from bad news about a company X?

Yes, learning a text classification model would be an approach to separate good news from bad news. This of course means, you have to supply (or crawl from the internet) text passages which you can present to the learner to make it able to learn a model.

Coming to the second part, a cross validation builds a mandatory part of almost all supervised learning tasks in which the performance of a model should be validated. Hence, it is suitable and recommendable in your application area as well, if you want to know how good your models can separate the good from the bad news.

Hope that helps,
Tobias