Dataset for Aspect Detection on Mobile reviews in H indi (2019)

Abstract: In recent years Opinion Mining has become one of the very interesting fields of Language Processing. To extract the gist of a sentence in a shorter and efficient manner is what opinion mining provides. In this paper we focus on detecting aspects for a particular domain. While relevant research work has been done in aspect detection in resource rich languages like English, we are trying to do the same in a relatively resource poor Hindi language. Here we present a corpus of mobile reviews which are labelled with carefully curated aspects. The motivation behind Aspect detection is to get information on a finer level about the data. In this paper we identify all aspects related to the gadget which are present on the reviews given online on various websites. We also propose baseline models to detect aspects in Hindi text after conducting various experiments.

## introduction over the last decade people tend to search for products online rather than physically on stores. this has resulted in a surge of online forums where reviews are available on various products, electronic gadgets being one of the more popular ones. but reading so many long reviews is very time consuming and there is no uniformity on the parameters of reviews. to solve this, research work has been done in this area in the form of aspect detection which helps to point out the key specifications of the product in a structured format. but the work is limited to only worldwide languages as english and french. for a multi-lingual country like india, we are still far away in getting these information in the native language. we aimed at creating a dataset in hindi which has the highest number of native speakers. the dataset is annotated with aspects for mobile reviews. an aspect is a word in a sentence which has some polarity associated with it. the aspect should hold major meaning of the sentence. following examples will state what aspect is: s1 : शाओमी रे डमी 4ए को पहली बार हाथ में ले ने पर यह आपको मे टल बॉडी का बना लगे गा । s1 : xiaomi redmi 4a ko pehli baar hath m lene par yeh aapko metal body ka bana lagega. aspect1 : "मे टल बॉडी" (metal body) which falls under the "िडज़ाइन" (design) category. the aspect shows importance by indicating how the mobile is built. s2: शाओमी रे डमी नोट में 2 गीगाहट्जर् ़ ऑक्टा -कोर क्वालकॉम स्नै पडर् ै गन 625 पर्ोसे सर का इस्ते माल हु आ है । s2: xiaomi redmi note m 2 gigahertz octacore qualcomm snapdragon 625 processor ka istemal hua hai. aspect:"ऑक्टा -कोर क्वालकॉम स्नै पडर् ै गन 625" (octa core qualcomm snapdragon) which falls under the "स्पे िसिफके शन" (specification) category . the aspect tells specifically tells the details of product. s3: अफसोस यह िक आप उन्हें हटा नहीं सकते । s3: afsos yeh ki aap unhe hata nahi sakte. aspect : "null" as there is no word which tells about any detail of the product. hence, it is classified under no aspect category. 2 related work major work has been done in aspect detection when it comes to resource rich languages like english. the work of aspect detection has also been followed by sentiments analysis which plays a major part in opinion mining. in 2014 semeval-task 4, maria pontiki (2014) provided the first dataset which con-aspect class in hindi aspect class in roman count सॉफ्टवे यर software 52 स्पे िसिफके शन और फ़ीचर specification aur feature 360 हमारा फ़ै सला hamara faisla 9 कै मरा और बै टरी लाइफ camera aur battery life 5 स्पे िसिफके शन और सॉफ्टवे यर specification aur software 137 कै मरा camera 76 परफॉमे र् ं स performance 826 लु क व बनावट look vah banawat 26 बै टरी लाइफ battery life 1 हमारा फै सला hamara faisla 300 कै मरा परफॉमे र् ं स camera performance 16 िडज़ाइन design 138 िडज़ाइन और लु क design aur look 168 null null 352 स्पे िसिफके शन specification 139 िडज़ाइन और िबल्ड design aur build 390 िडज़ाइन और िडस्प्ले design aur display 49 स्पे िसिफके शन , सॉफ्टवे यर और परफॉमे र् ं स specification, software aur performance 40 ## data creation as mentioned, earlier our work is on a specific domain. to build our corpus we scrapped data from various online forums with reviews on mobile phones. we extracted the text from the html data with the help of beautiful-soup library foot_0 in python. as our language was hindi, online reviews were very less for which we tried both dynamic and manual crawling of data. after crawling over 8 websites, we were able to get over 381 reviews. we retrieved 294 mobile reviews(37410 sentences) in a html format after extensive removal of noisy reviews. we had 294 html files which had raw data between different html tags. there was no uniformity in the reviews, even after extraction and tokenization of these reviews, many reviews had proper headings like specifications, performance, price, design under which two-three paragraphs of text was present. but there were many reviews without any headings. to make it uniform and bring it to sentence level rather than paragraph level, we assigned the heading as labels to every sentence appearing under that heading in the review. this was our first annotation strategy. while assigning heading as aspects, there were certain sentences which had no heading above them. such sentences were labelled as null. after this initial annotation, we had 18 classes of aspects in total. after doing analysis on our 18 classes, we observed a lot of overlapping between different classes. some classes had the same name, but due to spelling variations they were assigned different labels. ## experimental setup the main task was to predict aspects in every sentence in a review. we used different classifiers for the prediction task. we mostly experimented with machine learning models with 5-fold cross-validation as we had limited amount of data at our disposal. ## results the results are shown in ## observation from from the values of confusion matrix, we observed that class स्पे िसिफके शन(specification) has overshadowed classes null and कै मरा(camera). it shows that our model is not able to predict between the umbrella class and the child class accurately. ## conclusion and future work we annotated aspects for mobile reviews written in hindi as a part of this work. we also presented baseline models for automatic aspect identification in mobile reviews. the baseline models will help us to annotate more reviews semi-automatically and can then be integrated to improve our systems. we will explore more into neural network architecture and word embeddings. the next task in this area would be to annotate polarity of the aspects. we can also explore identifying the most informative reviews @xcite .