Excercise 1.
1. → Felipe bought a camera
→
The camera has three lenses
► Felipe bought a camera which has three lenses.
2. →
Jane is the woman
→
Jane is going to China next year
►
Jane is the woman who going to China next year.
3. →
Mr. Bryan looks very sad
→
Mr. Bryan’s team has lost the game
►
Mr. Bryan looks very sad that team has a lost the game.
4. →
The Doctor is with a patient
→
The patient’s leg was broken in an accident
►
The doctor is with a patient whose leg was broken in an accident.
5. →
James wrote a article
→
The article indicated that he disliked the President
►
James wrote a article that indicated that he disliked the President
Excercise 2.
Microblogging has emerged as a popular platform and a powerful
communication tool among people nowadays. A clear majority of people share
their opinions about various aspects of their lives online every day. Thus,
microblogging websites offer rich sources of data in order to perform sentiment
analysis and opinion mining. Because microblogging has emerged relatively
recently there are only some research works which are devoted to this field. In
this paper, the focus is on performing the task of sentiment analysis using
Twitter which is one of the most popular microblogging platforms. Twitter is a
very popular microblogging site where its users write status messages called
tweets to express themselves. These status updates mostly express their opinions
about various topics. The objective of this paper is to build a system that can
classify these Twitter status updates as positive, negative, or neutral with respect
to any query term thereby giving an idea about the overall sentiment of the
people towards that topic. This type of sentiment analysis is useful for advertisers, consumers researching a service or product, companies, governments,
marketers, or any organization who are researching public opinion.
(Masrani, manav., Poornalatha. (2017). Twitter Sentiment Analysis Using a Modified Naïve Bayes Algorithm).
Aufar Masruly
