Unit 3 Assignment 1 Opinion Mining

His opinion was obvious based on the title he chose for the article. The title was Child Sex Slave Locked in Wardrobe and Repeatedly Raped for Five Years. This showed his opinion because the reader already knows how he felt about this issue. It was the only article that calls Lauren a “child sex slave.” He also showed opinion when he repeated part of the title in his article by saying Lauren was used as a sex slave to satisfy her parents’ twisted desires (Jackson-Edwards). In the article Jackson-Edwards also let his opinion be known of how he felt about Lauren’s parents. Again he was the only one to call her parents “evil pedophiles.” Jackson-Edwards showed that he obviously felt hatred towards Lauren’s parents. In using the words, “sex slave,” and “evil pedophiles,” he stated more than just the facts. He let the reader know how he felt. The reader will then be more likely to feel those emotions as well. This is good because it will make the reader want to learn more and they will keep listening to find

Also, the analysis is the main strength of this method because in this option we can explain the whole story in our own words also we can argue with some points and with some assumptions that author made. We can agree and disagree with something.

Analysis skills are needed by everyone for every writer or speaker is not blunt or obvious with their (true) message. Many may have a secondary or hidden message due to social constraints or quite simply

These simple programs check what you've written and then count the amount of times a certain word appears, using a Bayesan algorithm that assigns a “weight” to each word and then compares the weight of the different categories – such as weak male, strong female, etc. - and then assigns a percentage-based result to your

Document opinion analysis is about classifying the overall document that have sentiment words expressed by the authors. The task is to determine whether a document is positive, negative or neutral (Wawre & Deshmukh). Document level categorization attempts to classify sentiments in web forum postings, blogs, movie and news

For example, article 2 delves deeper into the history of bullfighting and the beauty of this sport to give the reader a sense that this is a beautiful, historical, art form rather than a slaughtering that should be stopped. It states how Bullfighting is seen as a symbol of Spain that goes too far back in its tradition to be replaced. It states that the bull is a worthy adversary, and that it is also eaten so it’s death is not in vain. These differences in article 1 and 2 demonstrate that this genre is flexible and there are many ways to go about writing an opinion

web mining is a method of structure information from unstructured or semi-structured web data source. Corporations are using web mining as gears to collect data from the different website. The data is gathered and examined to design a website, which it can offer information from variety of websites. The business can raise the sales because the consumers have an aptitude to trace web users browsing manners to the mouse clicks. It allows a business to personalize facilities for the client.

Esuli and Sebastiani (2006) developed a WordNet of sentimental words called as SentiWordNet. The lexicon was

“Torture data long enough and it will confess. . . but may not tell the truth” (Turbin, Volonino, & Woods, 2015, p. 88). In the world of Big Data Analytics (BDA), companies who successfully harness the potential of big data are rewarded with valuable insight that could lead to a competitive advantage in their market sector. Consequently, it is imperative to successfully extract data from all relevant data sources that can provide answers to questions that companies set out to answer with BDA. One such source is data generated by social media (Schatten, Ševa, & Đurić, 2015). As such, this paper will review the findings of Schatten, Ševa, & Đurić’s(2015) article on how social web mining and big data can be utilized within the social

Working - Sharethrough headline analyse tool will help you to analyze topic potential. This headline analyzer tool analyse words choice, engagement and impression to generate result. Word choice can be use of passive voice and headline length etc.

First, we derive the collective opinion $O$ of a whole data sample $D$, using a SA method existing in the literature. Specifically, we adopt in this work the method SACI cite{jws2015}. SACI is relevant to our goal since it was originally proposed for estimating efficiently collective sentiment on data samples, instead of aggregating the sentiment derived for each individual document. Further, the authors demonstrated that SACI is more effective in estimating the collective opinion than aggregation-based SA methods. SACI represents $O$ as a distribution probability among the sentiment classes positive, negative and neutral. Thus, we split $D$ into time units of equal size (e.g., days, weeks, months). Then, we estimate the collective opinion $O_t$ using only the posts belonging to each distinct time unit $t$. Finally, we perform a visual inspection on the derived distributions. The more dynamic a domain, the more different are opinions estimated on distinct time units.looseness=-1

Opinions play a crucial role in the decision making process. Analysis in the field of making decisions and setting policies has shown that sentiment analysis and Opinion mining has become increasingly important in the field of Information Retrieval and Web analysis. In the past years, the growth of user generated data in web forums, social networking sites and other social platforms is tremendous, which diverts our study towards mining the opinions on web. In this paper, we have presented a novel methodology to classify the tweets and the complete opinion mining system is explained on the basis of survey and analysis. A flowchart has been proposed in which the overall picture of classification of twitter data has been proposed and accuracy of the evaluation strategies by various supervised learning algorithms has been evaluated. Review data is collected for various product domains from micro blogging sites like twitter, face book.

The assumption is that words and phrases mentioned most often are those reflecting important concerns in every communication. Therefore, quantitative content analysis starts with word frequencies, space measurements (column centimeters/inches in the case of newspapers), time counts (for radio and television time) and keyword frequencies. However, content analysis extends far beyond plain word counts, e.g. with Keyword In Context routines words can be analysed in their specific context to be disambiguated. Synonyms and homonyms can be isolated in accordance to linguistic properties of a language.

Preprocessing is actually a trail to improve text classification by removing worthiness information. In our work document preprocessing involve removing punctuation marks, numbers, words written in another language, normalize the documents by (replace the letter ("أ إ آ ") with (ا""), replace the letter (ء ؤ" ") with (""ا), and replace the letter("ى") with (""ا). Finally removing the stop words, which are words that can be found in any text like prepositions and pronouns. The rest of words are returned and are referred to as keywords or features. The number of these features is usually large for large documents and therefore some filtering can be applied to these features to reduce their number and remove redundant features.

v) Output: The main objective of sentiment analysis is to convert unstructured text into meaningful data. When the analysis is finished, the text results are displayed on graphs in the form of pie chart, bar chart and line graphs. Also time can be analyzed and can be graphically displayed constructing a sentiment time line with the chosen