Sentiment Classification over Opinionated Data Streams Through Informed Model Adaptation

2017 ◽  
pp. 369-381 ◽  
Author(s):  
Vasileios Iosifidis ◽  
Annina Oelschlager ◽  
Eirini Ntoutsi
Keyword(s):  
Data Streams ◽  
Sentiment Classification ◽  
Model Adaptation

scholarly journals Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World

2017 ◽  
Author(s):  
Sahil Garg ◽  
Irina Rish ◽  
Guillermo Cecchi ◽  
Aurelie Lozano
Keyword(s):  
Dictionary Learning ◽  
Data Streams ◽  
Empirical Evaluation ◽  
Real Life ◽  
Synthetic Data ◽  
Model Adaptation ◽  
Model Selection Approach ◽  
Online Dictionary Learning ◽  
Selection Approach ◽  
State Of Art

We address the problem of online model adaptation when learning representations from non-stationary data streams. Specifically, we focus here on online dictionary learning (i.e. sparse linear autoencoder), and propose a simple but effective online model selection approach involving “birth” (addition) and “death” (removal) of hidden units representing dictionary elements, in response to changing inputs; we draw inspiration from the adult neurogenesis phenomenon in the dentate gyrus of the hippocampus, known to be associated with better adaptation to new environments. Empirical evaluation on real-life datasets (images and text), as well as on synthetic data, demonstrates that the proposed approach can considerably outperform the state-of-art non-adaptive online sparse coding of [Mairal et al., 2009] in the presence of non-stationary data. Moreover, we identify certain data- and model properties associated with such improvements.

scholarly journals Handling concept drifts and limited label problems using semi-supervised combine-merge Gaussian mixture model

2021 ◽  
Vol 10 (6) ◽  
pp. 3361-3368
Author(s):  
Ibnu Daqiqil Id ◽  
Pardomuan Robinson Sihombing ◽  
Supratman Zakir
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
High Speed ◽  
Concept Drift ◽  
Model Performance ◽  
Gaussian Mixture ◽  
Model Adaptation ◽  
Model Accuracy ◽  
Concept Drifts ◽  
Do So

When predicting data streams, changes in data distribution may decrease model accuracy over time, thereby making the model obsolete. This phenomenon is known as concept drift. Detecting concept drifts and then adapting to them are critical operations to maintain model performance. However, model adaptation can only be made if labeled data is available. Labeling data is both costly and time-consuming because it has to be done by humans. Only part of the data can be labeled in the data stream because the data size is massive and appears at high speed. To solve these problems simultaneously, we apply a technique to update the model by employing both labeled and unlabeled instances to do so. The experiment results show that our proposed method can adapt to the concept drift with pseudo-labels and maintain its accuracy even though label availability is drastically reduced from 95% to 5%. The proposed method also has the highest overall accuracy and outperforms other methods in 5 of 10 datasets.

Improved Macro-clusters generation using Top-k shared Micro-clusters in Data Streams

2017 ◽  
Vol 7 (10) ◽  
pp. 52
Author(s):  
LAKSHMI PRANEETHA
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Bloom Filter ◽  
Scientific Applications ◽  
Pruning Algorithm ◽  
Density Data ◽  
Data Points ◽  
Short Time ◽  
Information Streams

Now-a-days data streams or information streams are gigantic and quick changing. The usage of information streams can fluctuate from basic logical, scientific applications to vital business and money related ones. The useful information is abstracted from the stream and represented in the form of micro-clusters in the online phase. In offline phase micro-clusters are merged to form the macro clusters. DBSTREAM technique captures the density between micro-clusters by means of a shared density graph in the online phase. The density data in this graph is then used in reclustering for improving the formation of clusters but DBSTREAM takes more time in handling the corrupted data points In this paper an early pruning algorithm is used before pre-processing of information and a bloom filter is used for recognizing the corrupted information. Our experiments on real time datasets shows that using this approach improves the efficiency of macro-clusters by 90% and increases the generation of more number of micro-clusters within in a short time.

SENTIMENT CLASSIFICATION OF THE LOCAL VISITORS' SOCIAL MEDIA REVIEWS

2018 ◽  
Vol 4 (26) ◽  
pp. 5534-5538
Author(s):  
Semra AKTAŞ POLAT
Keyword(s):  
Social Media ◽  
Sentiment Classification
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