Microblog Sentiment Orientation Detection Using User Interactive Relationship

The development and popularity of microblog have made sentiment analysis of tweets and Weibo an important research field. However, the characteristics of microblog message pose challenge for the sentiment analysis and mining. The existing approaches mostly focus on the message content and context information. In this paper, we propose a novel microblog sentiment analysis framework by incorporating the social interactive relationship factor in the content-based approach. By exploring the interactive relationship on social network based on posted messages, we build social interactive model to represent the opposition or acceptation behavior. Based on the interactive relationship model, the sentiment of microblog message with sparse emotion terms can be deduced and identified, and the sentiment uncertainty can be alleviated to some extent. Afterwards, we transform the classification problem into an optimization problem. Experimental results on Weibo data set indicate that the proposed method can outperform the baseline methods.

Download Full-text

Mapping research on the social impact of the arts: what characterises the field?

Open Research Europe ◽

10.12688/openreseurope.14147.1 ◽

2021 ◽

Vol 1 ◽

pp. 124

Author(s):

Sofia Lindström Sol ◽

Cia Gustrén ◽

Gustaf Nelhans ◽

Johan Eklund ◽

Jenny Johannisson ◽

...

Keyword(s):

Social Impact ◽

Public Funding ◽

Well Being ◽

Research Field ◽

Research Front ◽

Data Set ◽

Word Analysis ◽

The Arts ◽

Arts And Health ◽

The Social

This article explores the broad and undefined research field of “the social impact of the arts”. The effects of art and culture are often used as justification for public funding, but the research on these interventions and their effects is unclear. Using a co-word analysis of over 10,000 articles published between 1990 and 2020, we examined the characteristics of the field as we have operationalised it through our searches. Since 2015, the research field of “the social impact of art” has expanded and consists of different epistemologies and methodologies, summarised in largely overlapping subfields belonging to the social sciences/humanities, arts education, and arts and health/therapy. In formal or informal learning settings, studies of theatre/drama as an intervention to enhance skills, well-being, or knowledge among children are most common. A study of the research front, operationalised as the bibliographic coupling of the most cited articles in the data set, confirmed the co-word analysis and revealed new themes that together form the ground for insight into research on the social impact of the arts. As such, this article can inform discussions on the social value of the arts and culture.

Download Full-text

Reviewing Sentiment Analysis at the Shallow End

Transactions on Machine Learning and Artificial Intelligence ◽

10.14738/tmlai.84.8274 ◽

2020 ◽

Vol 8 (4) ◽

pp. 47-62

Author(s):

Francisca Oladipo ◽

Ogunsanya, F. B ◽

Musa, A. E. ◽

Ogbuju, E. E ◽

Ariwa, E.

Keyword(s):

Machine Learning ◽

Social Media ◽

Sentiment Analysis ◽

Information Exchange ◽

Training Data ◽

Data Set ◽

The Social ◽

Machine Learning Approach ◽

Media Space ◽

Social Media Platforms

The social media space has evolved into a large labyrinth of information exchange platform and due to the growth in the adoption of different social media platforms, there has been an increasing wave of interests in sentiment analysis as a paradigm for the mining and analysis of users’ opinions and sentiments based on their posts. In this paper, we present a review of contextual sentiment analysis on social media entries with a specific focus on Twitter. The sentimental analysis consists of two broad approaches which are machine learning which uses classification techniques to classify text and is further categorized into supervised learning and unsupervised learning; and the lexicon-based approach which uses a dictionary without using any test or training data set, unlike the machine learning approach.

Download Full-text

Cross-Modal Sentiment Sensing with Visual-Augmented Representation and Diverse Decision Fusion

Sensors ◽

10.3390/s22010074 ◽

2021 ◽

Vol 22 (1) ◽

pp. 74

Author(s):

Sun Zhang ◽

Bo Li ◽

Chunyong Yin

Keyword(s):

Sentiment Analysis ◽

Decision Fusion ◽

Visual Features ◽

Final Decision ◽

The Public ◽

Proposed Model ◽

The Social ◽

Interactive Relationship ◽

Multimodal Sentiment Analysis ◽

Hybrid Fusion

The rising use of online media has changed the social customs of the public. Users have become accustomed to sharing daily experiences and publishing personal opinions on social networks. Social data carrying emotion and attitude has provided significant decision support for numerous tasks in sentiment analysis. Conventional methods for sentiment classification only concern textual modality and are vulnerable to the multimodal scenario, while common multimodal approaches only focus on the interactive relationship among modalities without considering unique intra-modal information. A hybrid fusion network is proposed in this paper to capture both inter-modal and intra-modal features. Firstly, in the stage of representation fusion, a multi-head visual attention is proposed to extract accurate semantic and sentimental information from textual contents, with the guidance of visual features. Then, multiple base classifiers are trained to learn independent and diverse discriminative information from different modal representations in the stage of decision fusion. The final decision is determined based on fusing the decision supports from base classifiers via a decision fusion method. To improve the generalization of our hybrid fusion network, a similarity loss is employed to inject decision diversity into the whole model. Empiric results on five multimodal datasets have demonstrated that the proposed model achieves higher accuracy and better generalization capacity for multimodal sentiment analysis.

Download Full-text

PENGARUH PENYELENGGARAAN HAJI TERHADAP PENINGKATAN AKTIVITAS EKONOMl DI WILAYAH PROPINSI DKI JAKARTA

ALQALAM ◽

10.32678/alqalam.v30i2.1420 ◽

2013 ◽

Vol 30 (2) ◽

pp. 380

Author(s):

Chairul Akmal

Keyword(s):

Secondary Data ◽

Travel Agency ◽

Economic Activities ◽

Factors Affecting ◽

Regression Methods ◽

The Social ◽

Travel Agencies ◽

Relationship Model ◽

Small Medium Enterprises ◽

Medium Enterprises

This research analyzes some factors affecting economic activities in relation with the conduct of pilgrimage. Those factors are the pilgrimage cost, the amount of pilgrims, and the amount of pilgrimage officers. The objective of this research is to acquire the information of how each factor and all factors together affect the economic activities. This research also analyzes the effect of foods and drinks expenses, the effect of nonfoods and drinks expenses, and the effect of miscellaneous expenses on UMKM - Micro, Small, Medium enterprises' economic activities. This research is conducted in DKI Jakarta in 2007. The population of this research is the average economic activities in DKI Jakarta in 2007. There are 42 respondents (Banks), 157 respondents (travel agencies), and 50 respondents (UMKM - Micro, Small, Medium enterprises) which are taken as samples from the population using the purposive sampling method. The data is obtained by the researcher using questioners and secondary data which is taken from 1990-2007. The methodology used in this research is based on. the causal relationship model In testing the hypothesis of this research, the researcher uses the simple and multiple regression methods, and path analysis method. The significant rate a = 0,05 used in determining the interpretation of the statistic result. The data is processed using SPSS (Statistical Packages for the Social Sciences) version 12.00. The results of the analysis in the 1st equation -are (i) the effect of the pilgrimage cost on banks' revenues is quite strong, (ii) the effect of the pilgrimage cost on travel agencies' revenues is quite strong, (iii) the effect of the pilgrimage cost on UMKM - Micro, Small, Medium enterprises' revenues is weak. The results of the analysis in the 2nd equation are (i) the effect, of the amount of pilgrims on Banks' revenues is very weak, (ii) the effect of the amount of pilgrims on travel agencies' revenues is very weak, (iii) the effect of the amount of pilgrims on UMKM - Micro, Smal4 Medium enterprises' revenues is very weak. The results of the analysis in the 3rd equation are (i) the effect of the amount of pilgrimage officers on banks' revenues is very weak, (ii) the effect of the amount of pilgrimage officers on travel agencies' revenues is very weak, (iii) the effect of the amount officers on UMKM-Micro, Small Medium enterprises' revenues is very weak. The results of the analysis in the 4th equation are (i) the effect of all three factors which are the pilgrimage cost, the amount of pilgrims, and the amount of pilgrimage officers simultaneously on banks' revenues is very strong, (ii) The effect of all three factors which are pilgrimage costs, the amount of pilgrims, and the amount of pilgrimage officers simultaneously on travel agencies' revenues is strong, (iii) The effect of all three factors which are pilgrimage costs, the amount of pilgrims, and the amount of pilgrimage officers simultaneously on UMKM-Micro, Small Medium enterprises' revenues is strong. The result of the analysis in the 5th equation is the effect of foods and drinks expenses on UMKM-Micro, Small Medium enterprises' revenues is weak. In the 6th equation, the effect of nonfoods and drinks expenses on UMKM-Micro, small Medium enterprises' revenues is weak. In the 7th equation, the effect of miscellaneous expenses on UMKM - Micro, Small Medium enterprises' revenues is quite strong. In the 8th equation, the effect of all three factors which are the effect of foods and drinks expenses, the effect of nonfoods and drinks expenses, and the effect of miscellaneous expenses simultaneously on UMKM-Micro, Small Medium enterprises' revenues is quite strong. The implication of the research results mentioned above is the factors in the conduct of pilgrimage do increase the economic activities (Banks, Travel Agencies, and UMKM - Micro, Smal4 Medium enterprises) in DKI Jakarta. Therefore, considering that matter, the General Director of the conduct of pilgrimage division of Department of Religion Republic of Indonesia should determine the pilgrimage cost which is affordable, increase the service, and provide a good information system which will result in a better conduct of the pilgrimage. Key word: The Costs of Hajj, Hajj Officer, Travel Agency, UMKM

Download Full-text

The social wasp Vespula germanica (Fabricius) (Hymenoptera: Vespidae) population dynamics in England over 39 years.

The Entomologist s monthly magazine ◽

10.31184/m00138908.1542.3906 ◽

2018 ◽

Vol 154 (2) ◽

pp. 149-155

Author(s):

Michael Archer

Keyword(s):

Population Dynamics ◽

Population Dynamic ◽

Ecological Factors ◽

Social Wasp ◽

Data Sets ◽

Data Set ◽

Vespula Germanica ◽

The Social ◽

Minimum Number ◽

Suction Traps

1. Yearly records of worker Vespula germanica (Fabricius) taken in suction traps at Silwood Park (28 years) and at Rothamsted Research (39 years) are examined. 2. Using the autocorrelation function (ACF), a significant negative 1-year lag followed by a lesser non-significant positive 2-year lag was found in all, or parts of, each data set, indicating an underlying population dynamic of a 2-year cycle with a damped waveform. 3. The minimum number of years before the 2-year cycle with damped waveform was shown varied between 17 and 26, or was not found in some data sets. 4. Ecological factors delaying or preventing the occurrence of the 2-year cycle are considered.

Download Full-text

A Review on Sentiment Classification: Natural Language Understanding

Recent Patents on Engineering ◽

10.2174/1872212112666180731113353 ◽

2019 ◽

Vol 13 (1) ◽

pp. 20-27 ◽

Cited By ~ 1

Author(s):

Srishty Jindal ◽

Kamlesh Sharma

Keyword(s):

Natural Language ◽

Sentiment Analysis ◽

Social Networking Sites ◽

Natural Language Understanding ◽

Business Analytics ◽

Language Understanding ◽

Text Data ◽

Data Set ◽

Market Positioning ◽

Illegal Activities

Background: With the tremendous increase in the use of social networking sites for sharing the emotions, views, preferences etc. a huge volume of data and text is available on the internet, there comes the need for understanding the text and analysing the data to determine the exact intent behind the same for a greater good. This process of understanding the text and data involves loads of analytical methods, several phases and multiple techniques. Efficient use of these techniques is important for an effective and relevant understanding of the text/data. This analysis can in turn be very helpful in ecommerce for targeting audience, social media monitoring for anticipating the foul elements from society and take proactive actions to avoid unethical and illegal activities, business analytics, market positioning etc. Method: The goal is to understand the basic steps involved in analysing the text data which can be helpful in determining sentiments behind them. This review provides detailed description of steps involved in sentiment analysis with the recent research done. Patents related to sentiment analysis and classification are reviewed to throw some light in the work done related to the field. Results: Sentiment analysis determines the polarity behind the text data/review. This analysis helps in increasing the business revenue, e-health, or determining the behaviour of a person. Conclusion: This study helps in understanding the basic steps involved in natural language understanding. At each step there are multiple techniques that can be applied on data. Different classifiers provide variable accuracy depending upon the data set and classification technique used.

Download Full-text

Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

Journal Of Big Data ◽

10.1186/s40537-021-00488-w ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Yahya Albalawi ◽

Jim Buckley ◽

Nikola S. Nikolov

Keyword(s):

Social Media ◽

Deep Learning ◽

Comprehensive Evaluation ◽

Classification Problem ◽

Data Sets ◽

Word Embeddings ◽

Data Set ◽

Lower Accuracy ◽

Health Related ◽

The Impact

AbstractThis paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

Download Full-text

Has the Credibility of the Social Sciences Been Credibly Destroyed? Reanalyzing the “Many Analysts, One Data Set” Project

Socius Sociological Research for a Dynamic World ◽

10.1177/23780231211024421 ◽

2021 ◽

Vol 7 ◽

pp. 237802312110244

Author(s):

Katrin Auspurg ◽

Josef Brüderl

Keyword(s):

Social Sciences ◽

Causal Reasoning ◽

Research Question ◽

Social Science Research ◽

Science Research ◽

Skin Tone ◽

Data Set ◽

The Social ◽

The Many ◽

Definition Of

In 2018, Silberzahn, Uhlmann, Nosek, and colleagues published an article in which 29 teams analyzed the same research question with the same data: Are soccer referees more likely to give red cards to players with dark skin tone than light skin tone? The results obtained by the teams differed extensively. Many concluded from this widely noted exercise that the social sciences are not rigorous enough to provide definitive answers. In this article, we investigate why results diverged so much. We argue that the main reason was an unclear research question: Teams differed in their interpretation of the research question and therefore used diverse research designs and model specifications. We show by reanalyzing the data that with a clear research question, a precise definition of the parameter of interest, and theory-guided causal reasoning, results vary only within a narrow range. The broad conclusion of our reanalysis is that social science research needs to be more precise in its “estimands” to become credible.

Download Full-text