Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significantsource of user-generated content. The development of sentiment lexicons that can support languages ot...Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significantsource of user-generated content. The development of sentiment lexicons that can support languages other thanEnglish is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existingsentiment analysis systems focus on English, leaving a significant research gap in other languages due to limitedresources and tools. This research aims to address this gap by building a sentiment lexicon for local languages,which is then used with a machine learning algorithm for efficient sentiment analysis. In the first step, a lexiconis developed that includes five languages: Urdu, Roman Urdu, Pashto, Roman Pashto, and English. The sentimentscores from SentiWordNet are associated with each word in the lexicon to produce an effective sentiment score. Inthe second step, a naive Bayesian algorithm is applied to the developed lexicon for efficient sentiment analysis ofRoman Pashto. Both the sentiment lexicon and sentiment analysis steps were evaluated using information retrievalmetrics, with an accuracy score of 0.89 for the sentiment lexicon and 0.83 for the sentiment analysis. The resultsshowcase the potential for improving software engineering tasks related to user feedback analysis and productdevelopment.展开更多
Purpose–Data integration is to combine data residing at different sources and to provide the users with a unified interface of these data.An important issue on data integration is the existence of conflicts among the...Purpose–Data integration is to combine data residing at different sources and to provide the users with a unified interface of these data.An important issue on data integration is the existence of conflicts among the different data sources.Data sources may conflict with each other at data level,which is defined as data inconsistency.The purpose of this paper is to aim at this problem and propose a solution for data inconsistency in data integration.Design/methodology/approach–A relational data model extended with data source quality criteria is first defined.Then based on the proposed data model,a data inconsistency solution strategy is provided.To accomplish the strategy,fuzzy multi-attribute decision-making(MADM)approach based on data source quality criteria is applied to obtain the results.Finally,users feedbacks strategies are proposed to optimize the result of fuzzy MADM approach as the final data inconsistent solution.Findings–To evaluate the proposed method,the data obtained from the sensors are extracted.Some experiments are designed and performed to explain the effectiveness of the proposed strategy.The results substantiate that the solution has a better performance than the other methods on correctness,time cost and stability indicators.Practical implications–Since the inconsistent data collected from the sensors are pervasive,the proposed method can solve this problem and correct the wrong choice to some extent.Originality/value–In this paper,for the first time the authors study the effect of users feedbacks on integration results aiming at the inconsistent data.展开更多
Personal photo revisitation on smart phones is a common yet uneasy task for users due to the large volume of photos taken in daily life. Inspired by the human memory and its natural recall characteristics, we build a ...Personal photo revisitation on smart phones is a common yet uneasy task for users due to the large volume of photos taken in daily life. Inspired by the human memory and its natural recall characteristics, we build a personal photo revisitation tool, PhotoPrev, to facilitate users to revisit previous photos through associated memory cues. To mimic users＇ episodic memory recall, we present a way to automatically generate an abundance of related contextual metadata （e.g., weather, temperature） and organize them as context lattices for each photo in a life cycle. Meanwhile, photo content （e.g., object, text） is extracted and managed in a weighted term list, which corresponds to semantic memory. A threshold algorithm based photo revisitation framework for context- and content-based keyword search on a personal photo collection, together with a user feedback mechanism, is also given. We evaluate the scalability on a large synthetic dataset by crawling users＇ photos from Flickr, and a 12-week user study demonstrates the feasibility and effectiveness of our photo revisitation strategies.展开更多
基金Researchers supporting Project Number(RSPD2024R576),King Saud University,Riyadh,Saudi Arabia.
文摘Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significantsource of user-generated content. The development of sentiment lexicons that can support languages other thanEnglish is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existingsentiment analysis systems focus on English, leaving a significant research gap in other languages due to limitedresources and tools. This research aims to address this gap by building a sentiment lexicon for local languages,which is then used with a machine learning algorithm for efficient sentiment analysis. In the first step, a lexiconis developed that includes five languages: Urdu, Roman Urdu, Pashto, Roman Pashto, and English. The sentimentscores from SentiWordNet are associated with each word in the lexicon to produce an effective sentiment score. Inthe second step, a naive Bayesian algorithm is applied to the developed lexicon for efficient sentiment analysis ofRoman Pashto. Both the sentiment lexicon and sentiment analysis steps were evaluated using information retrievalmetrics, with an accuracy score of 0.89 for the sentiment lexicon and 0.83 for the sentiment analysis. The resultsshowcase the potential for improving software engineering tasks related to user feedback analysis and productdevelopment.
文摘Purpose–Data integration is to combine data residing at different sources and to provide the users with a unified interface of these data.An important issue on data integration is the existence of conflicts among the different data sources.Data sources may conflict with each other at data level,which is defined as data inconsistency.The purpose of this paper is to aim at this problem and propose a solution for data inconsistency in data integration.Design/methodology/approach–A relational data model extended with data source quality criteria is first defined.Then based on the proposed data model,a data inconsistency solution strategy is provided.To accomplish the strategy,fuzzy multi-attribute decision-making(MADM)approach based on data source quality criteria is applied to obtain the results.Finally,users feedbacks strategies are proposed to optimize the result of fuzzy MADM approach as the final data inconsistent solution.Findings–To evaluate the proposed method,the data obtained from the sensors are extracted.Some experiments are designed and performed to explain the effectiveness of the proposed strategy.The results substantiate that the solution has a better performance than the other methods on correctness,time cost and stability indicators.Practical implications–Since the inconsistent data collected from the sensors are pervasive,the proposed method can solve this problem and correct the wrong choice to some extent.Originality/value–In this paper,for the first time the authors study the effect of users feedbacks on integration results aiming at the inconsistent data.
基金The work was supported by the National Natural Science Foundation of China under Grant Nos. 61373022, 61073004, and the National Basic Research 973 Program of China under Grant No. 2011CB302203-2.
文摘Personal photo revisitation on smart phones is a common yet uneasy task for users due to the large volume of photos taken in daily life. Inspired by the human memory and its natural recall characteristics, we build a personal photo revisitation tool, PhotoPrev, to facilitate users to revisit previous photos through associated memory cues. To mimic users＇ episodic memory recall, we present a way to automatically generate an abundance of related contextual metadata （e.g., weather, temperature） and organize them as context lattices for each photo in a life cycle. Meanwhile, photo content （e.g., object, text） is extracted and managed in a weighted term list, which corresponds to semantic memory. A threshold algorithm based photo revisitation framework for context- and content-based keyword search on a personal photo collection, together with a user feedback mechanism, is also given. We evaluate the scalability on a large synthetic dataset by crawling users＇ photos from Flickr, and a 12-week user study demonstrates the feasibility and effectiveness of our photo revisitation strategies.
