Summarization and sentiment analysis from user health posts

Mane, Vinod L.; Panicker, Suja; Patil, Vidya B.

doi:10.1109/pervasive.2015.7087087

Cited by 18 publications

(2 citation statements)

References 11 publications

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“…In cases of more than one parameter, freq returns the number of co-occurrences of its parameters. The steps for association rule mining are as follows [3]: 1) Generation of association rules using a suitable algorithm.…”

Section: Support( X ) = P( X ) = Freq( X ) / N Confidence( X → Y ) = mentioning

confidence: 99%

Proposal of a System for Dynamic Management of Road Traffic Signals

C¹

2018

IJRASET

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Traffic congestion is one of the biggest problems that metropolitan cities are facing. The degree of the effects heavy traffic congestion can have is not very evident. It can have immense effect not only on one's safety but also on one's personal life. One of the major contributors for this is traffic jams caused by irregular and often longer durations of waiting time at traffic signal junctions. Finding a solution to this problem is imperative. Current technologies use wired and wireless sensors to optimize traffic light control but this method proves to be expensive and incurs high maintenance cost. This paper presents a new cost effective system for adjusting the traffic light waiting time using machine learning. We make use of previous years' traffic data of a city and come up with the appropriate number of seconds of waiting time based on patterns in traffic data.

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Section: Support( X ) = P( X ) = Freq( X ) / N Confidence( X → Y ) = mentioning

confidence: 99%

Proposal of a System for Dynamic Management of Road Traffic Signals

C¹

2018

IJRASET

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“…The applications of sentiment analysis are diverse and closely affecting our daily lives and decisions. In the healthcare field [1] [2] [3], the opinions published on health communities significantly help patients to find the right doctor for their cases, make the correspondence with their symptoms and take preventive measures. Also, doctors could adapt their prescriptions, schedules, and practices, even pharmaceutical companies benefit by analyzing the public effect of medications and planning studies based on patient attitudes.…”

Section: Introductionmentioning

confidence: 99%

MSTD: Moroccan Sentiment Twitter Dataset

Mihi¹,

Ait²,

El³

et al. 2020

IJACSA

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With the proliferation of social media and Internet accessibility, a massive amount of data has been produced. In most cases, the textual data available through the web comes mainly from people expressing their views in informal words. The Arabic language is one of the hardest Semitic languages to deal with because of its complex morphology. In this paper, a new contribution to the Arabic resources is presented as a large Moroccan dataset retrieved from Twitter and carefully annotated by native speakers. For the best of our knowledge, this dataset is the largest Moroccan dataset for sentiment analysis. It is distinguished by its size, its quality given by the commitment of annotators, and its accessibility for the research community. Furthermore, the MSTD (Moroccan Sentiment Twitter Dataset) is benchmarked through experiments carried out for 4-way classification as well as polarity classification (positive, negative). Various machine-learning algorithms are combined to feature extraction techniques to reach optimal settings. This work also presents the effect of stemming and lemmatization on the improvement of the obtained accuracies.

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