Systems and methods that enable usage of a modified Bayesian classification to enable sentiment estimation in social media. In some embodiments, events are classified, and the words described sequentially to the event are processed. The system processes historical information and current information to identify the most likely subclass a document belongs to, to help in the estimation of sentiment of a social media user.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for using social media data, from multiple sources, by usage of a modified Bayesian classification to enable sentiment estimation of the users of the social media, the method comprising the steps of: a) defining two discrete events, one of the events being the classification of an event, and another event being analysis of the words appearing sequentially to the event in a document; b) in the analysis of the words appearing sequentially to the event in a document, parsing the incoming message into words list, which includes all punctuation; c) then collecting one word as a probability of the current message: ∑ i = 1 n   P  ( W i ) = ∑ i = 1 n   W i  /  S S = ∑ i = 1 w   C i where, W is the number single word existed in system, and Ci is total count for Wi. 2. A method for using social media data, from multiple sources, by usage of a modified Bayesian classification to enable sentiment estimation of the users of the social media according to claim 1, the method further comprising the steps of: after analyzing for a single word, executing the following on two words combinations: 2  ∑ i = 1 n - 1   P  ( W i ⋂ W i + 1 ) = ∑ i = 1 n - 1   C w i  w i + 1  /  S 2 where, S2 is total two-word appearances as counted in the system. 3. A method for using social media data, from multiple sources, by usage of a modified Bayesian classification to enable sentiment estimation of the users of the social media according to claim 1, the method further comprising the steps of: after analyzing for two words, executing the following for three word combinations: For a 3 words combination: 4  ∑ i = 1 n - 2   P  ( W i ⋂ W i + 1 ⋂ W i + 2 ) = ∑ i = 1 n - 1   C w i  w i + 1  w i + 2  /  S 3 . where the coefficient before the summation is equal 2m-1 where m is the words in combination. 4. A method for using social media data, from multiple sources, by usage of a modified Bayesian classification to enable sentiment estimation of the users of the social media, the method comprising the steps of: a) defining two discrete events, one of the events being the classification of an event, and another event being analysis of the words appearing sequentially to the event in a document; b) in the analysis of the words appearing sequentially to the event in a document, parsing the incoming message into words list, which includes all punctuation; c) then collecting word probability in a current message by the following: 2 m - 1  ∑ i = 1 n - m   P  ( W i ⋂ W i + 1 ⋂ W i + 2  …  ⋂ i + m ) = ∑ i = 1 n - 1   C w i  w i + 1  …   w m  /  S m where, W is the number single word existed in system, and Ci is total count for Wi; where, S2 is total word appearances as counted in the system; and where the coefficient before the summation is equal 2m-1 where m is the words in combination. 5. A method for using social media data, from multiple sources, by usage of a modified Bayesian classification to enable sentiment estimation of the users of the social media, the method comprising the steps of: wherein the coordinators of a message and group are calculated based word frequency occurred in message and group of messages with a similarity coefficient expressed as vectors normalized correlation coefficient, the similarity coefficient is calculated as vectors normalized correlation coefficients as follows: cos  ( g , m ) = ∑ i = 1 n   g i  m i ∑ i = 1 n   g i 2  ∑ i = 1 n   m i 2 wherein gi is i'th word frequency for one of learned categories and mi is the i'th word frequency for current message.