=Paper=
{{Paper
|id=Vol-2540/paper42
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-2540/FAIR2019_paper_6.pdf
|volume=Vol-2540
}}
==None==
https://ceur-ws.org/Vol-2540/FAIR2019_paper_6.pdf
Negation handling for Amharic sentiment classification
Girma Neshir1, Andreas Rauber2 and Solomon Atnafu3
1
Addis Ababa University, IT Doctoral Program, Ethiopia, girma1978@gmail.com
2
Technical University of Vienna, Institute of Information Systems Engineering, Austria, rau-
ber@ifs.tuwien.ac.at
3
Addis Ababa University, Department of Computer Science, Ethiopia,
solomon.atnafu@aau.edu.et
Introduction: Due to the advancement of World Wide Web technology, users usually
express their feelings, emotions and opinions as comments in response to the posted
news, photo, audio and video. Currently, opinionated sources are increasing in lan-
guages other than English. However, Amharic sentiment analysis researches are very
few as it has no sufficient linguistic resources for linguistic preprocessing and senti-
ment analysis. There are several challenges in lexicon based sentiment analysis. One
of these is that handling negation in the text. The most common approach for negation
handling is carried out relying on negation keywords. However, it is complex to iden-
tify the scope of negation where the process of correctly identifying the part of the
text affected by the presence of negation word. Negation Handling(NH) is never stud-
ied in Amharic language to the best of our knowledge. Thus, this research develops an
automatic method to handle negation and combined with char ngram features for
Amharic sentiment classification. The research questions to be addressed in this work
are as follows: (a) how can we automatically detect negation words in Amharic texts?
(b) how can we design a framework for handling negation in Amharic sentiment anal-
ysis? (c) how to capture char level ngram features for improving Amharic sentiment
analysis in Social media(e..g. Facebook) and (d) how can we evaluate the perfor-
mance of the framework?
Proposed Approach: As part of preprocessing, we normalized not only all Amharic
words in the Amharic News Comments but also handling entries of Amharic Senti-
ment Lexicon by replacing varied alphabets of the same sound with identical symbols.
Moreover, a stemmer is applied after negation identification is completed. As
Amharic is morphologically rich, to reduce the mismatch of Amharic words during
string comparison operation. We also used stemming for this purpose. The proposed
framework consists of components including preprocessing and sentiment score cal-
culation using negation detection and machine learning using char level ngrams fea-
tures. For more detail, the proposed framework is shown in Fig. 1 of Appendix. To
compute sentiment score using negation detection, for each Amharic news comment,
Ci, if each stemmed word wij is found in either of the Amharic Sentiment lexicons
(Manual, SOCAL, SWN) [7], then the sentiment score s ij is retrieved. sij and its posi-
tion index in the comment is stored. To compute the sentiment of the comment, we
apply positional weighting inversion if the comment contains any negation clue. If
negation clue is not found, the score of the word is simply added. For more detail, the
negation handling algorithm is depicted in Listing 1 of Appendix. Besides lexicon
based negation handling approach, the usefulness of character aware language models
is well suited to apply for language identification, reducing of text feature sparse di-
Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0)
2
mensionality, helps to handle spelling errors, abbreviations, special characters, etc.
[6]. That is why we proposed character level ngram approaches to reduce and address
these issues for Amharic facebook news comments’ sentiment classification rather
than word level ngram approaches. For example, the negation carrying Amharic word
“አልወደውም”/ “I do not like him”/ has 2-gram character level features includes አ-አል-
ልወ-ወደ-ደው-ውም-ም, 3-gram character level features are አ- አል-አልወ-ልወደ-ወደው-
ደውም-ውም-ም, etc. The negation marker/morpheme/ አል- is detected as feature of the
negation word/“አልወደውም”/. Thus, prior to char ngram based sentiment classifica-
tion, we partition the Annotated Amharic Facebook News Comment corpus into train-
ing and testing sets. Logistic regression(LR) and Naïve Bayesian(NB) models are
built relying on the term-frequency inverse document frequency(tfidf) of char level
and word level(baseline) bi-gram and tri-gram features of training set.
Results: For Amharic Sentiment Classification, the results of the accuracy of the indi-
vidual and the combined models on the test set are presented in detail in Table 1 of
Appendix. The results in Table 1 show that negation handling algorithm outperforms
very well (acc. 86.2%) than the performance of character level and word level based
machine learning models for classifying sentiment of Amharic texts. On the other
hand, character level ngram based classifier is more useful for classifying Amharic
Sentiment than word level ngram models (baseline) (accuracy of 95.27). Finally, the
hybrid model is obtained by combining negation handling approach and char ngram
models (NB+LR). This hybrid model outperforms with accuracy of 98% than the
other models and its combinations. Yet, it is quite difficult to find why errors are gen -
erated in predicting sentiment category of Amharic Facebook Comments. For exam-
ple the facebook comment, 'በቃል የሚነገሩነገሮችን በተግባር እንዲፈፀሙልን እንፈልጋለን፣ በተግባር እንዲፈፀሙልን እንፈልጋለን፣ እን በተግባር እንዲፈፀሙልን እንፈልጋለን፣ ዲፈፀሙልን በተግባር እንዲፈፀሙልን እንፈልጋለን፣ እን በተግባር እንዲፈፀሙልን እንፈልጋለን፣ ፈልጋለን በተግባር እንዲፈፀሙልን እንፈልጋለን፣ ፣ '/
We need to see accomplished in practice that we heard in words/ is wrongly pre-
dicted. This comment does not express any opinion. This kind of comment represents
wishes that someone wants it to be done, but not necessarily expressing sentiment.
Further researches needs to be carried out to reduce the source of errors in predicting
sentiment class of Amharic comments. Our recent findings is a good starting point to
improve the performance of Amharic sentiment analysis in facebook news comments.
Fine tuning char ngram features shows suitableness and flexible for sentiment analy-
sis of resource limited language (e.g. Amharic) than word level ngram models
Conclusions: In general, extensive linguistic resources are expensive to build senti-
ment classification on the less dominant languages (e.g. Amharic). To reduce this
problem, we proposed negation handling approach and char ngram approach for Sen-
timent analysis of Amharic face book news comments. So far, we have seen that the
proposed approach still lacks accuracy of Amharic sentiment classification. The ap-
proach potentially does not sufficiently capture the language specific features that
help to identify the sentiment class of Amharic news comment in social media. Fur-
ther work should be performed to reduce the amount of errors in sentiment analysis of
Amharic facebook news comments. To address these issues, we may need to consider
char ngram embedding features from corpus of the same domain(e.g. Facebook news
comments). Besides, Amharic negation scope identification and handling is recom-
mended to be investigated for further researches.
3
References
1. Rizkiana Amalia, Moch Arif Bijaksana, and Dhinta Darmantoro. Negation handling in
sentiment classification using rule-based adapted from indonesian language syntactic for
indonesian text in twitter. In Journal of Physics: Conference Series, volume 971, page
012039. IOP Publishing, 2018.
2. Amna Asmi and Tanko Ishaya. Negation identification and calculation in sentiment analy-
sis. In The second international conference on advances in information mining and man-
agement, pages 1--7, 2012.
3. Claudia Diamantini, Alex Mircoli, and Domenico Potena. A negation handling technique
for sentiment analysis. In 2016 International Conference on Collaboration Technologies
and Systems (CTS), pages 188--195. IEEE, 2016.
4. Martine Enger, Erik Velldal, and Lilja Øvrelid. An open-source tool for negation detec-
tion: a maximum-margin approach. In Proceedings of the Workshop Computational Se-
mantics Beyond Events and Roles, pages 64--69, 2017.
5. Umar Farooq, Hasan Mansoor, Antoine Nongaillard, Yacine Ouzrout, and Muhammad
Abdul Qadir. Negation handling in sentiment analysis at sentence level. JCP, 12(5):470--
478, 2017.
6. Bas Heerschop, Paul van Iterson, Alexander Hogenboom, Flavius Frasincar, and Uzay
Kaymak. Accounting for negation in sentiment analysis. In 11th DutchBelgian Information
Retrieval Workshop (DIR 2011), pages 38--39, 2011.
7. Girma Neshir Alemneh, Andreas Rauber, and Solomon Atnafu. Dictionary Based Amharic
Sentiment Lexicon Generation, pages 311--326. 08 2019.
4
Appendix: List of figures, tables and algorithm listings
Fig. 1 Proposed Framework for Negation Handling
Listing 1. Snap shot Algorithm for Amharic Negation Handling
Table 1. depicts the classification performance of the proposed framework