An Unsupervised Learning Approach for Automatically to Categorize Potential Suicide Messages in Social Media In this paper, we present an approach to categorize potential suicide messages in social media which is based on unsupervised learning. Our approach has five phases: the first two correspond to data acquisition and preprocessing where texts available in a corpus for suicide detection were taken and converted into a structured format; in the third phase, similarity between texts are computed using semantic similarity measures; traditional clustering algorithms were used to identify categories of potential suicide messages in the fourth phase; and, in last phase, using validation metrics we verified the usefulness of our approach to replicate the allocation of text into categories as in the original corpus data. Computational results showed that our approach is able to replicate the grouping of messages labeled as xe2x80x9cNo riskxe2x80x9d and xe2x80x9cRiskxe2x80x9d in average rates of 79 % and 87 % and rates up 13 % and 9 % in alert levels for English and Spanish, respectively.

Twitter Analysis for Depression on Social Networks based on Sentiment and Stress Detecting words that express negativity in a social media message is one step towards detecting depressive moods. To understand if a Twitter user could exhibit depression over a period of time, we applied techniques in stages to discover words that are negative in expression. Existing methods either use a single step or a data subset, whereas we applied a multistep approach which allowed us to identify potential users and then discover the words that expressed negativity by these users. We address some Twitter specific characteristics in our research. One of which is that Twitter data can be very large, hence our desire to be able to process the data efficiently. The other is that due to its enforced character limitation, the style of writing makes interpreting and obtaining the semantic meaning of the words more challenging. Results show that the sentiment of these words can be obtained and scored efficiently as the computation on these dataset were narrowed to only these selected users. We also obtained the stress scores which correlated well with negative sentiment expressed in the content. This work shows that by first identifying users and then using methods to discover words can be a very effective technique.