The Perception of Manobo Tribe on Different Aspects of Life: Their Effects on the Living and Social Conditions in San Luis, Agusan Del Sur

The study attempted to determine the perception of the Manobo tribe and their effect on their living and social conditions in Barangay Sta. Ines, San Luis Agusan del Sur. The descriptive method was used in this study describes the perception among the Manobo tribe on different aspects of life: their living and social conditions.  This study was conducted in barangay Sta. Ines, san Luis Agusan Del Sur.  There were only 195 of them who were taken as the respondents and researcher used the standardized questionnaire which were taken from the book of Mendez and used by the previous researchers. Hence, no validation was needed.  The random sampling design was used in this study in selecting the respondents. On the level of perception among the Manobo tribe, the Manobos agree on their belief in god as supported with the total average weighted mean ranging from 2.01 to 2.73 or agree.  They strongly agree to get involved in social gathering as evidenced with total weighted mean of 2.21 or agree.  They strongly agree on good aspiration as evidenced with average weighted mean of 2.71 or agree.  They agree on the family practices as supported with the average weighted mean of 2.45 or agree.  They strongly agree on good family upbringing accompanied with an average weighted mean of 2.57 or strongly agree.  They also strongly agree on how to treat visitors accompanied with an average weighted mean of 2.79 or strongly agree. On the living conditions, the Manobos rank 1 that they do not depend their main source of income but they work hard to have additional income for their family needs.  They also rank 1 that their house is made up of wood, Nipa and bamboos for the shelter of their family and the Manobo rank 1 that they usually used herbal for their natural medicine but they send the sick person to the hospital for the health of the family. On the social conditions, the Manobos strongly agree to establish a neighborhood relationship with an average weighted mean of 2.78 or strongly agree.  They also agree to establish kinship relationship and community relationship as indicated with an average weighted mean of 2.84 and 2.78 or strongly agree respectively. On the effect of perception on different aspects of life, belief in God, social; gathering, family practices, family upbringing and aspiration are best predictors in the financial condition of Manobo.  Aspiration, family practices, treating visitors, wedding practices and social gathering are best predictors in neighborhood relationship.  Wedding practices, treating visitors, aspiration, family practices, social gathering, family upbringing and belief in God as the best predictors in kinship relationship Family upbringing, aspiration, and family practices are best predictors in community relationship.

A Robust Framework of Sentiment Analysis for Online Customer Reviews and Blogs

The Web 2.0 has dramatically changed people‟s communication style. It is a great move toward more community oriented, highly collaborative, interactive and responsive Web. Today we are not only using the Internet but we are part of this global network. Social media sites became the world‟s largest virtual community, where people express their views about products, events and services, anytime from anywhere. These views have great impact on community thinking and decisions. The most flourished feature of this era is the rising of blogging which provides resourceful and open way to anyone, anywhere. These data sources provide the rich basis for sentiment analysis. The statistics show that 80% of consumers have changed their decisions about purchase based on negative reviews found online. The study found that blogs are 63% more likely to influence purchase decisions than magazines. Evaluation of social media has powered interest in sentiment analysis. There exist two main approaches for extracting sentiment automatically, the lexicon-based approach and statistical or machine learning approach. The later approach demands a lot of training data to learn lexical items that express sentiment and its performance drops when the same classifiers is used in a different domain. The main focus of this work is to develop a lexicon-based framework for automatic classification of blogs and reviews with respect to their semantic orientation. This method consists of three major components: Sentiment analysis, Slang‟s detection and scoring, and Context-aware spelling corrector. Lexicon-based methods for sentiment analysis are robust, give good performance in cross-domain and can be easily boosted with additional source of knowledge. It performs well on blog posting, reviews and also a preferable classifier for handling contextual valence shifters. Irrespective of these merits no single lexicon can perform in an optimal way all the time. This method uses a dynamic, updateable and comprehensive lexicon based on existing opinion lexicons, dictionaries and other machine-readable resources to classify the user-generated contents into positive, negative and neutral polarity. vii Slangs and spelling correction are two vital elements for sentiment analysis because slang and misspelled word may affect the sentiment score. These two issues were handled using Web resources and Statistical language model. The proposed work was implemented, and evaluated with different datasets of reviews and blogs. The empirical results show that the proposed work outperforms the existing, related methods and achieves 90.3% accuracy on average. This method showed high accuracy in binary classification. All the three components of the proposed method performed well with different domains.