Patent AU-2021100563-A4 (AU)
Title: An Active Learning Sentiment Analysis Framework for Mobile Phone Review Analysis using Deep Learning to Reduce Human Annotation
Inventor: MURALIDHAR RATHAN
IPC: G06F40/30
Filed: 2021-01-29
Granted: 2021-05-20
Abstract: The sentiment analysis framework created focuses on performing sentiment 
analysis by minimizing the human effort in annotation using active learning.  
The framework is created with the intent to particularly analyze mobile phone 
reviews with aspects such as "battery", "camera", "display", "performance" and 
so on. The model however, can be used for any other domain by changing the 
keywords required for each aspect of the domain. The first step is to perform 
cleaning of data by splitting each review into phrases, converting it to lower 
case, removing unneeded special characters and URLs, segmenting text into 
words, removal of stopwords, lemmatization, correcting spellings, classifying of 
emojis and emoticons to words bearing sentiments. Followed by this, the 
framework aims to remove all the neutral text statements hidden within the 
reviews. This is performed using a combination of Bing Liu and Vader lexicon.  
The top most repeated words within the neutral statements are identified and 
presented to the annotator who classifies these words as either positive, negative 
or neutral and fed back to the lexicon, thereby updating it. The updated lexicon 
is utilized to perform classification again, removing neutral tweets. This time 
though, all the reviews consisting of words identified by the annotator as 
positive or negative within neutral statements are retained. The framework then 
considers a small amount of text corpus for human annotation (approximately 
5000 text reviews as a rule of thumb to cover as many words as possible that are 
relevant for text classification) within each aspect. Once the deep learning 
model using long short term memory is trained with this data, the next 5000 text 
reviews are tested by the model. For each review tested with confidence of less 
than 75% i.e. closer to decision boundary, is provided to the annotator for 
labeling. Upon manual labeling of a select few reviews only, these 5000 tested 
reviews are updated to the training dataset with the model refit to test on the 
next 5000 reviews. This is repeated to ensure that the annotator does not
annotate all the reviews manually. Along with this, the neutral tweets are 
removed without an extensive human intervention required.
FIGURES 
Reprocessing of Iraining Ls I M 
testing Ls I M model 
the reviews of an eModelf by usr a agabnt other 
apctingdataset suetanually 
reviews reviews 
Fg e: o o a wh testNo Term the model 
as 
finalized model for 
p n chrc n the aspect 
yes 
Add the tested Annttatinng selected 
dataset back to reviews from testing 
training dataset set manually 
Figure 1: Flow of data which indicates the steps involved overall within the 
sentiment analysis framework.  
Splitting each review Convertiing each Removal of special Rem-ovall of 
|into phrases. phrase into lower characters and stoprwords 
case U~ 
Identifying most Neutral| Statement Eli and Emotilcon Lemmatizatilon of 
frequent non neutral -eIdentifilcation using -9Classilfication eachh review 
words by oracle Lexicon 
Addition of non Neutral Statement 
neutra Iwords to the Identification 
ad 
Lexicon 
Figure 2: Flow of data which indicates the steps involved pre-processing of 
data.