Intelligent Systems (Natural language processing, Sentiment analysis Assignment

Intelligent Systems (Natural language processing, Sentiment analysis and Text mining ) - Assignment Example In this area, computations are involved and they end up in a natural language capable of processing opinions. It therefore give as detailed study into mood or emotion recognition, relevance computation, ranking, identifying source, giving genre distinction, and a summarization of the opinion (Pang & lee, 2008). Moreover, Pang & Lee (2008) states that in sentimental analysis texts need to be mapped to respective labels from a defined data set or through placing it from one end to another on continuum. Knowing above, a topic was to be investigated to determine the effectiveness of these analyses. The topic chosen included â€Å"saber tooth desktop† and in the rating, it occurred that 87% of the comment were positive and only 13% accounted for the negative. I downloaded it using sentiment140.com. However, reading through the tweets, it occurred to me that the facility was never accurate. It could never detect sarcastic messages, shortened words and it only analyzed the English pa rt of the messages. Moreover, from the data of about four sets, correlation was sought out in order to determine the accuracy of the method and it was noted that for the positive tweets there was an F harmonic of about 6.5% and for the negatives it showed 4.2%. Consequently, this is an indication that sentiment gave a dependable result though they are not 100% accurate. Moreover, the above topic could be downloaded directly using tweeter API. One needs to have a tweeter account, create an application, from the application, one is offered machine readable consumer key access token, and consumer secrets and from those, one can receive tweeter updates on any website they specified and coded with the relevant details given when sign in for a tweeter API account. From the gotten results using tweeter Prolog-WordNet libraries, it indicated that there is a correlation between the sent tweets and the message they were conveying however at a given sentiment polarity. Moreover, the extraction this time round had a higher number of tweets and gives a considerable proportion of 72% for the positive tweets and a 28% for the negative. As a result, it showed how the various entities or concepts were directly linked to positivity or negativity of the sentiment. It analyzed that a good percentage as it is expected of positivity was correct as well as that of negativity. However, it incorporated some percentages of negative side to the positive and those of positive to negative. In consequence, it may be argued that, even though the second process is much demanding in acquiring tweets and then analyzing them, it is much accurate than the first process especially in the case involving huge volumes of data. Moreover, the gotten data could be analyzed deeper to according to either it being positive and negative. It could be analyzed into who it involved-the company, workers, sales staff and any other person involved. Moreover, it was noted that for the two applied types, they give out results related to features of the search. For the music or sports related, there is one where conversion of search results is higher. Part Two Natural language processing (NPL) is the part of computer studies that deals with artificial intelligence especially involving interactions of machine-computers-languages and the linguistic part of human. It basically tries to give