Comparative Analysis of Link-based and Content-based Methods for Opinion Mining in Persian language

Many online platforms for movie streaming have emerged nowadays in Indonesia with the development of technology. Streaming platforms that are widely used by the public are Netflix, Disney+, HBO Go, WeTV, Vidio. A lot of comparisons between streaming platforms have become a topic of discussion on social media. The opinions expressed by users of the streaming platform greatly affect other users who want to watch the movie. The transmission of opinions from users becomes positive and negative comments that are received by the public and spread widely on social media. Opinions that contain both positive and negative comments become a problem and can affect other users who want to watch movies through streaming platforms. The opinions of users about the streaming platform can be analyzed through sentiment analysis. At the stage of data collection using scrapy framework tools with Python, as much as 1,500 lines of text are preprocessed. LDA method to present topics that contain representative words. Classification using the Support Vector Machine (SVM) gets more positive comments than negative comments. The test results obtained an accuracy of 0.82, recall of 0.8, F1 score of 0.79, and precision of 0.8.

Ubiquitous access to internet has resulted in more and more people going online to get their daily dose of news. In a 2010 survey conducted by the Pew Project for Excellence in Journalism, 41% of the respondents said they get most of their news online, 10% more than those who said they got most of their news from a newspaper. A lot of socio-technical factors have contributed to this phenomenal rise in adoption of online news in recent years. One of the biggest reasons why people are increasingly reading news online is because it facilitates discussion with peers (Nguyen 2010), offering different viewpoints which aid in forming a rounded personal opinion about the news story. The Pew survey found that 37% of online news users (and 51% of 18-29 year olds) think that commenting on news stories is an important feature to have. A lot of people tend to shape their opinion by reading discussion comments, reflective articles, blogs and even tweets about the news. Hence, an increasing number of people rely on online sources of news – be it news websites or news aggregator services like Digg, Reddit, Google Reader, Flipboard, Pulse etc. The problem with these news websites and aggregators is that the only way people can gather public opinion is by actively searching through the endless stream of comments and feeds, filtering out spam (which is a growing problem) and then reading the relevant posts. A top trending story on Twitter will typically see multiple tweets per second, and keeping up with the rapid flow of incoming tweets is quite cumbersome and cognitively taxing. Hence it becomes increasingly difficult and time consuming for someone who wants to get the pulse of the people affected by a news story. Furthermore, in certain scenarios people might want to look at more fine grained opinions. Currently, there is no elegant way to extract geographic and demographic impact of a news story. What is the public sentiment in Indonesia about the Arab Spring? How did the public opinion about the Wikileaks disclosures change as the story unfolded during the course of a year? It is very difficult and tedious to observe such patterns using the currently available news providers. This work attempts to solve these problems by proposing a news aggregator platform which pulls news stories from various sources and also aggregates public responses, reflections, opinions and sentiments associated with those stories. This data is presented in ways that are easily understandable so readers can make better sense of the stories unfolding across the globe. Such a news aggregator platform that gathers and display public opinion and sentiments about a story, must deal with various challenges – 1. Opinions are very subjective. Different people feel about a story in different ways. With such an enormous amount of diverse opinions and subjectivity, how can we possibly aggregate the responses into something that makes sense as a whole? ________________________________ Acknowledgements: Prof. Yardi, S. for the guidance Sethi, P. (2013). Public opinion aggregation by annotation and tagging of online news stories. iConference 2013 Proceedings (pp. 891-894). doi:10.9776/13453 Copyright is held by the author. iConference 2013 February 12-15, 2013 Fort Worth, TX, USA 892 2. There isn’t really a unified web standard for expressing opinion (in textual form). Some people tweet in 140 characters, while others write elaborate blog posts. Some websites employ tags which a reader can use to define and classify their public opinion, while others rely on threaded comments and comment ranking systems. How can a platform be flexible enough to adapt to all these varied standards so that it can extract valuable data from various sources? Perhaps the platform can create a new standard of expression on the web which is flexible and comprehensive enough to be used to express diverse views about every news story in the world. 3. How to filter out spam while extracting public opinion? 4. Once the platform has access to the data it needs, how should it be displayed to the reader in a way that makes sense? What forms of visualizations, illustrations and graphical representations can be employed to give the reader a holistic view of how people feel about a story? 5. How can the platform determine and convey effects of geographical, demographic and temporal variations as the story unfolds? These are just a few out of possibly many issues which must be dealt with. Previous research on similar public opinion aggregation services has greatly focused on natural language processing, data mining and text categorization and clustering. Xiaojun (2010) proposed a framework for crawling the web for comments and applying various data mining algorithms on the data to extract relevant information. Diakopoulos and Shamma (2010) used tweets posted in conjunction with the live presidential debate between Barack Obama and John McCain to gauge public opinion. Brody and Diakopoulos (2011) studied the use of word lengthening to detect sentiment in microblogs. This research proposes a solution – The Opinionated Reader, which relies on sentiment tags and annotations associated with a news story. The essential idea is to create a commenting, discussion and sharing plug-in which can be used by news websites and aggregators as a commenting solution for their news pages. Users wanting to share or comment on a news story through the plug-in are asked to tag the news story with sentiment tags and annotate the story with their reaction (happy/positive or sad/negative). These tags and annotations are stored, aggregated and linked to each news story. A mobile application provides the front-end interface for users to access the news stories and the aggregated sentiment associated with each story. The basic architecture is explained as follows: The Opinionated Reader – Mobile/Tablet App The app fetches news articles from various web sources, based on the interests and preferences configured by the user. In every news article, a portion of the screen real estate is reserved for Opinions which shows graphical visualizations and illustrations of the public opinion surrounding the news story. These visualizations include: A Sentiment Graph indicating the popular tags associated with the story (E.g.: “Shocking”, “Inspiring”, “Amusing” etc.). See Figure 1 for example visualization. A Positivity Graph which plots the level of positivity associated with the story on a time scale from when the news broke. See Figure 2 for example visualization. Figure 1. Example Sentiment Graph Figure 2. Example Positivity Graph iConference 2013 February 12-15, 2013 Fort Worth, TX, USA 893 The user can choose to see these visualizations for a particular time period in the evolution history of the news story, or for a specific country. The app also facilitates people to tag and annotate news articles from within its interface. The Opinionated Reader – Commenting and Sharing Web Plug-in These days, a common way of adding discussion and commenting functionality to news websites is by using 3 rd party services (like DISQUS). The Opinionated Reader is a similar service which can be embedded into the news articles of various news websites to enable commenting and sharing. When someone wishes to comment on an article, the comment is directed through this plug-in, which allows the users to annotate the article with the sentiment tags and reactions along with their comments. The Opinionated Reader saves this information along with the commenter’s location and date of comment (See Figure 3). Figure 3. The Opinionated Reader Web Plug-in 'Add Comment' dialog mockup The Opinionated Reader – Back-end The Back-end maintains a database of news items extracted from RSS feeds of various news websites. Each news article is linked with the sentiment tags and reaction/positivity annotations extracted from the comments and annotations gathered by the commenting plug-in. This data is used by the mobile/tablet app to generate visualizations (Sentiment Graph and Positivity Graph). The back-end also performs data mining on the tags and annotations for geographies and tracks the opinions across time. The back end system responds to queries received from the mobile app with the news story and associated tags and annotations, which are then rendered by the mobile app for the user. iConference 2013 February 12-15, 2013 Fort Worth, TX, USA 894 Discussion and Conclusion This design idea is still in a nascent state and has long hours of research, brainstorming, designing and development to go before it can be realized into something tangible. Twitter has grown exponentially in importance as a news source and it would be vastly valuable to integrate Twitter with The Opinionated Reader. Possibilities include use of special hash tags and natural language processing of tweets to extract public sentiment. The current design supports only two reaction annotations – positive and negative. Not every news story fits this annotation paradigm. Further research about human reactions to news stories might unveil interesting insights which would help zero in on a more robust annotation rubric. Lastly, since this service is envisioned to be non-curated and non-moderated, the value served by the app depends on the users themselves. Greater adoption will lead to more annotations and tags, which translates into a more accurate public opinion as presented to the user.

Technological developments provide significant changes in the field of journalism, especially in the news by the mass media. The online news portal currently provides a comment column feature for news readers to comment on the existing news. This study aims to (1) look at the effect of reader comments on students' perceptions of controversial news about campus through the LINE Today feature, (2) analyze the difference in influence between positive comments and negative comments on students' perceptions of campus controversy news through the LINE Today feature. This study uses a quantitative approach with a questionnaire method to the research population. The sample is students of the Department of Communication, Faculty of Social and Political Sciences, Hasanuddin University who have accounts on the LINE application, totaling 199 people with the total sampling method. Regression analysis technique was used to see the effect of reader comments on student perceptions. To compare the effect of positive and negative reader comments, a comparative analysis technique is used, namely variance. The results showed that (1) There was a significant effect of reader comments on students' perceptions of campus controversy news through the LINE Today feature using both positive and negative stimuli; (2) There is a difference in the effect of positive and negative comments on students' perceptions of campus controversy news through the LINE Today feature.stimuli have a greater influence on student perceptions

Reviews are important for visitors who will travel because of the information found information about the destination. To get a more precise classification method, this experiment compared the Naive Bayes method and the Naive Bayes method with PSO. This experiment used 50 positive reviews and 50 negative reviews. Different accuracy was obtained from the two experiments. Accuracy obtained for the analysis of Indonesian public opinion sentiments toward tourism TMII uses Naive Bayes by 70% while the accuracy for Indonesian public public sentiment analysis of tourism TMII and PSO is 94.02% .

ObjectivesPrior research has demonstrated that men and women emergency medicine (EM) residents receive similar numerical evaluations at the beginning of residency, but that women receive significantly lower scores than men in their final year. To better understand the emergence of this gender gap in evaluations we examined discrepancies between numerical scores and the sentiment of attached textual comments.MethodsThis multicenter, longitudinal, retrospective cohort study took place at four geographically diverse academic EM training programs across the United States from July 1, 2013–July 1, 2015 using a real-time, mobile-based, direct-observation evaluation tool. We used complementary quantitative and qualitative methods to analyze 11,845 combined numerical and textual evaluations made by 151 attending physicians (94 men and 57 women) during real-time, direct observations of 202 residents (135 men and 67 women).ResultsNumerical scores were more strongly positively correlated with positive sentiment of the textual comment for men (r = 0.38, P < 0.001) compared to women (r = −0.26, P < 0.04); more strongly negatively correlated with mixed (r = −0.39, P < 0.001) and negative (r = −0.46, P < 0.001) sentiment for men compared to women (r = −0.13, P < 0.28) for mixed sentiment (r = −0.22, P < 0.08) for negative; and women were around 11% more likely to receive positive comments alongside lower scores, and negative or mixed comments alongside higher scores. Additionally, on average, men received slightly more positive comments in postgraduate year (PGY)-3 than in PGY-1 and fewer mixed and negative comments, while women received fewer positive and negative comments in PGY-3 than PGY-1 and almost the same number of mixed comments.ConclusionWomen EM residents received more inconsistent evaluations than men EM residents at two levels: 1) inconsistency between numerical scores and sentiment of textual comments; and 2) inconsistency in the expected career trajectory of improvement over time. These findings reveal gender inequality in how attendings evaluate residents and suggest that attendings should be trained to provide all residents with feedback that is clear, consistent, and helpful, regardless of resident gender.

Content Propagation in Online Social Networks

In recent years, discriminative learning methods have widely been used in various areas of Natural Language Processing (NLP). These methods achieve the best performance, when the set of training and testing samples have the same distribution. However, in many applications of NLP, the lack of labeled datasets for some domains is a serious challenge. In such conditions, we need to develop a model based on domains with rich labeled instances and apply it to the domain with no labeled instances. In this research, a method for sentiment classification of opinions into positive and negative groups, which represent the users' feelings, is offered based on multi-source transfer learning. The proposed method here employs Spectral Feature Alignment algorithm to adapt different domains. Furthermore, according to the Majority Voting, accuracy is assigned to classifications trained on different domains based on the Majority Voting Error. Ultimately, decisions are made for each classification based on the calculated error. The Amazon datasets for four different categories, each of which contains 1000 positive and 1000 negative samples, are exploited to train the proposed model. Meanwhile, each category includes unlabeled samples that are used to select pivot features. The accuracy values of 85.5%, 86.4%, 83.5% and 90.1% obtained for Electronics, DVD, Books and Kitchen domains respectively, show the effectiveness of the proposed method compared with similar methods.

Twitter is one of the most popular micro-blogging platforms where users publish their thoughts and opinions and much attention is paid to explore sentiment of these opinions. This paper focuses on the characteristic of Twitter, tweets and supervised machine-learning method for Twitter Sentiment Analysis. Discussion focuses on the following issues: access to the tweets and creating a database, the process of cleaning the database and process of tweets classification into positive and negative groups. The TSA process is presented in Python by simplified architecture.

Category:Practice ManagementIntroduction/Purpose:Utilization by patients of physician rating websites continues to expand. There is limited information on how these websites function and influence patient perception and physicians’ practices. No study has specifically investigated online ratings and comments of orthopedic foot and ankle surgeons. In this study, we identified what factors impact online ratings and comments for this subset of surgeons.Methods:A total of 210 orthopedic foot and ankle surgeons in or near metropolitan areas were randomly selected from the American Orthopaedic of Foot & Ankle Society (AOFAS) website. Demographic information, ratings (number of stars), and comments were reviewed on the three most visited public domain physician ratings websites: HealthGrades.com (HealthGrades), Vitals.com (Vitals), and Ratemds.com (Ratemds). Content differences between positive and negative comments were evaluated.Results:Orthopedic foot and ankle surgeons had a mean rating of 4.03 ± 0.57 out of 5 stars. A high percentage (84%) of the total number of ratings were either 1-star (17%) or 5-stars (67%). Most positive comments related to outcome, physician personality, and communication while most negative comments related to outcome, bedside manner, and waiting time. Chi-square analysis revealed statistically significant proportions of positive comments pertaining to surgeon-dependent factors (eg, physician personality, knowledge, skills) and of negative comments concerning surgeon-independent factors (eg, waiting time, logistics).Conclusion:This is the first study examining online ratings and written comments of orthopedic foot and ankle surgeons. Surgeons had a generally favorable rating and were likely to have positive comments. Patients were likely to write positive comments about surgeon personality and communication, and negative comments pertaining to bedside manner and waiting time. Knowledge and management of online content may allow surgeons to improve patient satisfaction and online ratings.

Social media has become a one-stop shop for consuming news and expressing political views. Politics has become increasingly emotional, and the ensuing polarization has created echo chambers that favor narratives and stories that repeat only one point of view. In this article, we investigated the role of political activity through Twitter (now ‘X’) engagement as a predictor of destructive fires and deforestation in the Brazilian Legal Amazon (BLA). We used a machine learning approach based on sentiment analysis and satellite data. To test the consistency of the sentiment analysis, we compared the timing of messages related to fire and deforestation events with daily fire data from satellites. When comparing positive and negative comments about fires in the BLA, the results showed that the best model for predicting fire outbreaks is the decision tree regressor. We found evidence that positive comments about agriculture, industry, and the Amazon rainforest in response to speeches and statements by high-ranking Brazilian politicians tend to induce positive comments about fire outbreaks and deforestation. These comments then become good predictors of fire outbreaks with a 6-day lag. These results support the view that high-ranking politicians have enormous power to influence damaging events that can have severe impacts on communities, the environment, and the economy. Brazil has seen an unprecedented increase in deforestation and fires in the Amazon rainforest in recent years. Our findings contribute to the growing literature on the role of social media in real-world events and how machine learning approaches can be used to address this class of problems.

Tapera is a program provided by the Indonesian government with the aim of providing poor people with decent housing, by charging all working classes with salaries above the Regional Minimum Wage of 3% for Tapera contributions. Even though it seems that this program aims to help poor people, this has given rise to a new polemic, which can be seen from some people who don't like this, as can be seen from several negative comments on social media, one of which is X or Twitter. The purpose of writing this paper is to find out whether the sentiment of the TAPERA policy is positive or negative for Indonesians citizens that affected by the policy, with the aim that the author can provide recommendations for government also encourage the usage of Social Media Analytics on government sector in Indonesia, even the result of the sentiment analysis is majority positive or majority negative. This research is conducted by collecting data from Twitter and then processed in such a way, and later will be checked for sentiment analysis with the naive bayes method. From 742 datasets that got from Twitter (after getting sortied, previously, 1680 data was get), it shows that there are 651 tweets that have negative sentiments, and 91 tweets have positive sentiments, which shows how people are disappointed with the TAPERA policy.

Sentiment analysis is very perplexing and massive issue in the field of social data mining. Twitter is one of the mostly used social media where people discuss on various issues in a dense way. The tweets about a particular topic give peoples' views, opinions, orientations, inclinations about that topic. In this work, we have used pre-labeled (with positive, negative and neutral opinion) tweets on particular topics for sentiment classification. Opinion score of each tweet is calculated using feature vectors. These opinion score is used to classify the tweets into positive, negative and neutral classes. Then using various machine learning classifiers the accuracy of predicted classification with respect to actual classification is being calculated and compared using supervised learning model. Along with building a sentiment classification model, analysis of tweets is being carried out by visualizing the wordcloud of tweets using R.

Nowadays, there are various types of cosmetic products and brands. Searching and studying information about cosmetic products from reviews are more important because most people use comments from others to help make purchase decisions. They will have trouble with a lot of cosmetic product reviews. Moreover, it takes a long time to search and read all, and it is difficult to make decisions. Therefore, this paper proposed satisfactory conclusion for cosmetic product review comments to analyze the positive and negative information about various cosmetic products by sentiment analysis. The implemented methods use Naive Bay Classifier as a machine learning to automatically classify positive or negative comments on cosmetic products. The testing results show that the classification application has high accuracy and precision on both positive and negative comments. However, it is necessary to use positive and negative data in a similar proportion to train the classification application for efficiency result.

Iran: Scripts, Old Persian, Aramaic, Avestan

Data is said to be the new oil. This applies to textual data as well. The amount of plain textual data on the Internet is abundant, and it is still growing. However, similar to oil, raw data is not valuable by itself. Its value and potential are availed through further accurate processing and analysis. And this is where Natural Language Processing (NLP) and its procedures come into the picture. Since NLP tasks require standardized and high-quality inputs for high efficiency and performance, raw textual data requires at least a basic level of cleaning and standardization. Therefore, text normalization - a process of transforming noisy data into improved, i. e., a standard representation - is often a prerequisite for a variety of NLP tasks, including information extraction, machine translation, or sentiment analysis. The Persian language, being the 5th content language of the Web, can be a great source of diverse information. However, several language-specific characteristics hinder the potential application of Persian data. The focus of this chapter lies in describing challenges that the Persian language poses for NLP and evaluating the impact of normalizing a large Persian corpus for one of the downstream NLP tasks - the discovery of multiword expressions (MWEs).