scholarly journals Acquiring Sentiment from Twitter using Supervised Learning and Lexicon-based Techniques

2016 ◽  
Vol 15 (1) ◽  
pp. 63-80
Author(s):  
Jitrlada ROJRATANAVIJIT ◽  
Preecha VICHITTHAMAROS ◽  
Sukanya PHONGSUPHAP
Keyword(s):  
Supervised Learning ◽  
Mixed Method ◽  
Short Length ◽  
Writing Systems ◽  
Learning Techniques ◽  
Average Accuracy ◽  
The Difference ◽  
Tweet Classification ◽  
Source Of Information ◽  
Processing Steps

The emergence of Twitter in Thailand has given millions of users a platform to express and share their opinions about products and services, among other subjects, and so Twitter is considered to be a rich source of information for companies to understand their customers by extracting and analyzing sentiment from Tweets. This offers companies a fast and effective way to monitor public opinions on their brands, products, services, etc. However, sentiment analysis performed on Thai Tweets has challenges brought about by language-related issues, such as the difference in writing systems between Thai and English, short-length messages, slang words, and word usage variation. This research paper focuses on Tweet classification and on solving data sparsity issues. We propose a mixed method of supervised learning techniques and lexicon-based techniques to filter Thai opinions and to then classify them into positive, negative, or neutral sentiments. The proposed method includes a number of pre-processing steps before the text is fed to the classifier. Experimental results showed that the proposed method overcame previous limitations from other studies and was very effective in most cases. The average accuracy was 84.80 %, with 82.42 % precision, 83.88 % recall, and 82.97 % F-measure.

scholarly journals Conflict matrix as a mechanism of identifying the conflict in emotions of written text

2020 ◽  
Vol 9 (2) ◽  
pp. 541
Author(s):  
Abbasi Mohsin Manshad ◽  
Abbasi Anees Qumar ◽  
Beltiukov Anatoly Petrovich ◽  
Hussain Lal.
Keyword(s):  
Information Technology ◽  
Supervised Learning ◽  
Confusion Matrix ◽  
Main Idea ◽  
Major Product ◽  
Conflict Analysis ◽  
Written Text ◽  
Learning Techniques ◽  
Truth Value ◽  
Source Of Information

Text is a major source of information and is considered as a mechanism of communicating emotions and ideas. The emotion and their analysis from written text gained popularity over recent decades. It has been credited to the growth of information technology and the rapid increase in availability of internet around the globe. In this work, the main idea is to identify the conflict in emotions that exist in the written text. The use of conflict analysis is to identify the contradictory views of people about an object or a topic of discussion. Its existence in text however complicates the process of analysis of emotions from text. This paper describes a mechanism in which the emotions in each pair of sentence are considered as conflicting to each other. The emotional orientation of each pair of sentence is observed to identify the truth-value of the proposed conflict hypothesis. The result of the analysis is summarized using the Conflict matrix. Conflict matrix is a major product of this research that is used to identify the conflicting emotions in text and to measure their characteristics. The results of the experiment were analyzed using the supervised learning techniques along with the Confusion matrix. In methodology and conclusion sections of the paper, the results are discussed in detail.  

scholarly journals Ablation Analysis to Select Wearable Sensors for Classifying Standing, Walking, and Running

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 194
Author(s):  
Sarah Gonzalez ◽  
Paul Stegall ◽  
Harvey Edwards ◽  
Leia Stirling ◽  
Ho Chit Siu
Keyword(s):  
Activity Recognition ◽  
Principal Components ◽  
Classification Accuracy ◽  
Wearable Sensors ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Measurement Units ◽  
The Difference

The field of human activity recognition (HAR) often utilizes wearable sensors and machine learning techniques in order to identify the actions of the subject. This paper considers the activity recognition of walking and running while using a support vector machine (SVM) that was trained on principal components derived from wearable sensor data. An ablation analysis is performed in order to select the subset of sensors that yield the highest classification accuracy. The paper also compares principal components across trials to inform the similarity of the trials. Five subjects were instructed to perform standing, walking, running, and sprinting on a self-paced treadmill, and the data were recorded while using surface electromyography sensors (sEMGs), inertial measurement units (IMUs), and force plates. When all of the sensors were included, the SVM had over 90% classification accuracy using only the first three principal components of the data with the classes of stand, walk, and run/sprint (combined run and sprint class). It was found that sensors that were placed only on the lower leg produce higher accuracies than sensors placed on the upper leg. There was a small decrease in accuracy when the force plates are ablated, but the difference may not be operationally relevant. Using only accelerometers without sEMGs was shown to decrease the accuracy of the SVM.

scholarly journals SVM-Based Bearing Anomaly Identification with Self-Tuning Network-Fuzzy Robust Proportional Multi Integral and Smart Autoregressive Model

2021 ◽  
Vol 11 (6) ◽  
pp. 2784
Author(s):  
Shahnaz TayebiHaghighi ◽  
Insoo Koo
Keyword(s):  
Machine Learning ◽  
Variable Structure ◽  
Rolling Element Bearing ◽  
Crack Identification ◽  
Original Signal ◽  
Signal Modeling ◽  
Residual Signal ◽  
Average Accuracy ◽  
Self Tuning ◽  
The Difference

In this paper, the combination of an indirect self-tuning observer, smart signal modeling, and machine learning-based classification is proposed for rolling element bearing (REB) anomaly identification. The proposed scheme has three main stages. In the first stage, the original signal is resampled, and the root mean square (RMS) signal is extracted from it. In the second stage, the normal resampled RMS signal is approximated using the AutoRegressive with eXternal Uncertainty (ARXU) technique. Moreover, the nonlinearity of the bearing signal is solved using the combination of the ARXU and the machine learning-based regression, which is called AMRXU. After signal modeling by AMRXU, the RMS resampled signal is estimated using a combination of the proportional multi-integral (PMI) technique, the variable structure (VS) Lyapunov technique, and a self-tuning network-fuzzy system (SNFS). Finally, in the third stage, the difference between the original signal and the estimated one is calculated to generate the residual signal. A machine learning-based classification technique is utilized to classify the residual signal. The Case Western Reserve University (CWRU) dataset is used to evaluate anomaly identification performance of the proposed scheme. Regarding the experimental results, the average accuracy for REB crack identification is 98.65%, 97.7%, 97.35%, and 97.67%, respectively, when the motor torque loads are 0-hp, 1-hp, 2-hp, and 3-hp.

Evolution of Stress During Formation of Titanium Disilicide by Rta and Tube Furnace Annealing

1990 ◽  
Vol 202 ◽  
Author(s):  
J.F. Jongste ◽  
O.B. Loopstra ◽  
G.C.A.M. Janssen ◽  
S. Radelaar
Keyword(s):  
Integrated Circuit ◽  
Thermal Processing ◽  
Reaction Rates ◽  
Titanium Disilicide ◽  
Furnace Annealing ◽  
Integrated Circuit Fabrication ◽  
Annealing Step ◽  
Circuit Fabrication ◽  
The Difference ◽  
Processing Steps

Integrated circuit fabrication consists of many processing steps: e.g. lithography, etching, implantation and metallization. Some of these processes are combined with thermal processing. Heat treatments require special attention because previous fabrication steps may be influenced: e.g. dopant profiles may be deteriorated. The amount of interference of an annealing step with a former process is determined by the ratio of the reaction rates (and hence by the difference in activation energies).

FG-LiquID

2021 ◽  
Vol 5 (3) ◽  
pp. 1-27
Author(s):  
Yumeng Liang ◽  
Anfu Zhou ◽  
Huanhuan Zhang ◽  
Xinzhe Wen ◽  
Huadong Ma
Keyword(s):  
Alcohol Concentration ◽  
Coarse Grained ◽  
Robust Identification ◽  
Fine Grained ◽  
Fresh Milk ◽  
Average Accuracy ◽  
Coarse Grained Material ◽  
Potential Applications ◽  
Fine Grained Material ◽  
The Difference

Contact-less liquid identification via wireless sensing has diverse potential applications in our daily life, such as identifying alcohol content in liquids, distinguishing spoiled and fresh milk, and even detecting water contamination. Recent works have verified the feasibility of utilizing mmWave radar to perform coarse-grained material identification, e.g., discriminating liquid and carpet. However, they do not fully exploit the sensing limits of mmWave in terms of fine-grained material classification. In this paper, we propose FG-LiquID, an accurate and robust system for fine-grained liquid identification. To achieve the desired fine granularity, FG-LiquID first focuses on the small but informative region of the mmWave spectrum, so as to extract the most discriminative features of liquids. Then we design a novel neural network, which uncovers and leverages the hidden signal patterns across multiple antennas on mmWave sensors. In this way, FG-LiquID learns to calibrate signals and finally eliminate the adverse effect of location interference caused by minor displacement/rotation of the liquid container, which ensures robust identification towards daily usage scenarios. Extensive experimental results using a custom-build prototype demonstrate that FG-LiquID can accurately distinguish 30 different liquids with an average accuracy of 97%, under 5 different scenarios. More importantly, it can discriminate quite similar liquids, such as liquors with the difference of only 1% alcohol concentration by volume.

scholarly journals Ecological Interactions and the Netflix Problem

2016 ◽  
Author(s):  
Philippe Desjardins-Proulx ◽  
Idaline Laigle ◽  
Timothée Poisot ◽  
Dominique Gravel
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Random Forests ◽  
Species Interactions ◽  
Similarity Measures ◽  
Theoretical Models ◽  
Machine Learning Techniques ◽  
Nearest Neighbour ◽  
Ecological Interactions ◽  
Learning Techniques

0AbstractSpecies interactions are a key component of ecosystems but we generally have an incomplete picture of who-eats-who in a given community. Different techniques have been devised to predict species interactions using theoretical models or abundances. Here, we explore the K nearest neighbour approach, with a special emphasis on recommendation, along with other machine learning techniques. Recommenders are algorithms developed for companies like Netflix to predict if a customer would like a product given the preferences of similar customers. These machine learning techniques are well-suited to study binary ecological interactions since they focus on positive-only data. We also explore how the K nearest neighbour approach can be used with both positive and negative information, in which case the goal of the algorithm is to fill missing entries from a matrix (imputation). By removing a prey from a predator, we find that recommenders can guess the missing prey around 50% of the times on the first try, with up to 881 possibilities. Traits do not improve significantly the results for the K nearest neighbour, although a simple test with a supervised learning approach (random forests) show we can predict interactions with high accuracy using only three traits per species. This result shows that binary interactions can be predicted without regard to the ecological community given only three variables: body mass and two variables for the species’ phylogeny. These techniques are complementary, as recommenders can predict interactions in the absence of traits, using only information about other species’ interactions, while supervised learning algorithms such as random forests base their predictions on traits only but do not exploit other species’ interactions. Further work should focus on developing custom similarity measures specialized to ecology to improve the KNN algorithms and using richer data to capture indirect relationships between species.

scholarly journals Audio-visual media to improve sexual-reproduction health knowledge among adolescent

2020 ◽  
Vol 9 (1) ◽  
pp. 138
Author(s):  
Sitti Nur Djannah ◽  
Sulistyawati Sulistyawati ◽  
Tri Wahyuni Sukesi ◽  
Surahma Asti Mulasari ◽  
Fatwa Tentama
Keyword(s):  
Sexual Reproduction ◽  
Junior High School ◽  
Health Knowledge ◽  
Visual Media ◽  
Wilcoxon Test ◽  
Post Intervention ◽  
Before And After ◽  
The Mean ◽  
The Difference ◽  
Source Of Information

<span>Lacking knowledge among adolescents affects their understanding of some problems related to sexual-reproduction health. Electronic media recognized as the favored source of information for adolescents. This research aimed to assess the effect of audio-visual media to the increasing of sexual-reproduction knowledge. We conducted a before and after without control informal experimental study design into 153 students in the 1st-3rd grade of junior high school. The effect of the intervention was assessed through the difference between pre- and post-intervention by using the Wilcoxon test. The mean score of the respondent pre and post-intervention was significantly increasing. The audiovisual increased the knowledge of the adolescent regarding sexual-reproduction health</span>

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