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Title: Corpus Based Amharic Sentiment Lexicon Generation

Paper content:
Sentiment classification is an active research area with several applications including analysis of political opinions, classifying comments, movie reviews, news reviews and product reviews.
To employ rule based sentiment classification, we require sentiment lexicons.
However, manual construction of sentiment lexicon is time consuming and costly for resource-limited languages.
To bypass manual development time and costs, we tried to build Amharic Sentiment Lexicons relying on corpus based approach.
The intention of this approach is to handle sentiment terms specific to Amharic language from Amharic Corpus.
Small set of seed terms are manually prepared from three parts of speech such as noun, adjective and verb.
We developed algorithms for constructing Amharic sentiment lexicons automatically from Amharic news corpus.
Corpus based approach is proposed relying on the word co-occurrence distributional embedding including frequency based embedding (i.e. Positive Point-wise Mutual Information PPMI).
First we build word-context unigram frequency count matrix and transform it to point-wise mutual Information matrix.
Using this matrix, we computed the cosine distance of mean vector of seed lists and each word in the corpus vocabulary.
Based on the threshold value, the top closest words to the mean vector of seed list are added to the lexicon.
Then the mean vector of the new sentiment seed list is updated and process is repeated until we get sufficient terms in the lexicon.
Using PPMI with threshold value of 100 and 200, we got corpus based Amharic Sentiment lexicons of size 1811 and 3794 respectively by expanding 519 seeds.
Finally, the lexicon generated in corpus based approach is evaluated.
Most of sentiment mining research papers are associated to English languages.
Linguistic computational resources in languages other than English are limited.
Amharic is one of resource limited languages.
Due to the advancement of World Wide Web, Amharic opinionated texts is increasing in size.
To manage prediction of sentiment orientation towards a particular object or service is crucial for business intelligence, government intelligence, market intelligence, or support decision making.
For carrying out Amharic sentiment classification, the availability of sentiment lexicons is crucial.
To-date, there are two generated Amharic sentiment lexicons.
These are manually generated lexicon(1000) (Gebremeskel, 2010) and dictionary based Amharic SWN and SOCAL lexicons (Neshir Alemneh et al., 2019) .
However, dictionary based generated lexicons has short-comings in that it has difficulty in capturing cultural connotation and language specific features of the language.
For example, Amharic words which are spoken culturally and used to express opinions will not be obtained from dictionary based sentiment lexicons.
The word ጉርሻ/"feed in other people with hands which expresses love and live in harmony with others"/ in the Amharic text: "እንደ ጉርሻ ግን የሚያግባባን የለም. . . ጉርሻ እኮ አንዱ ለሌላው የማጉረስ ተግባር ብቻ አይደለም፤ በተጠቀለለው እንጀራ ውስጥ ፍቅር አለ፣ መተሳሰብ አለ፣ አክብሮት አለ።" has positive connotation or positive sentiment.
But the dictionary meaning of the word ጉርሻ is "bonus".
This is far away from the cultural connotation that it is intended to represent and express.
We assumed that such kind of culture (or language specific) words are found in a collection of Amharic texts.
However, dictionary based lexicons has short comings to capture sentiment terms which has strong ties to language and culture specific connotations of Amharic.
Thus, this work builds corpus based algorithm to handle language and culture specific words in the lexicons.
However, it could probably be impossible to handle all the words in the language as the corpus is a limited resource in almost all less resourced languages like Amharic.
But still it is possible to build sentiment lexicons in particular domain where large amount of Amharic corpus is available.
Due to this reason, the lexicon built using this approach is usually used for lexicon based sentiment analysis in the same domain from which it is built.
The research questions to be addressed utilizing this approach are: (1) How can we build an approach to generate Amharic Sentiment Lexicon from corpus?
(2)How do we evaluate the validity and quality of the generated lexicon?
In this work, we set this approach to build Amharic polarity lexicons in automatic way relying on Amharic corpora which is mentioned shortly.
The corpora are collected from different local news media organizations and also from facebook news' comments and you tube video comments to extend and enhance corpus size to capture sentiment terms into the generated PPMI based lexicon.
Using corpus based approach, Amharic sentiment lexicon is built where it allows finding domain dependent opinions which might not be possible by sentiment lexicon generated using dictionary based approach.
In this section, we have attempted to develop new approaches to bootstrapping relying on word-context semantic space representation of large Amharic corpora.
Creating a sentiment lexicon generation is not an objective process.
The generated lexicon is dependent on the task it is applied.
Thus, in this work we have seen that it is possible to create Sentiment lexicon for low resourced languages from corpus.
This captures the language specific features and connotations related to the culture where the language is spoken.
This can not be handled using dictionary based approach that propagates labels from resource rich languages.
To the best of our knowledge, the the PPMI based approach to generate Amharic Sentiment lexicon form corpus is performed for first time for Amharic language with almost minimal costs and time.
Thus, the generated lexicons can be used in combination with other sentiment lexicons to enhance the performance of sentiment classifications in Amharic language.
The approach is a generic approach which can be adapted to other resource limited languages to reduce cost of human annotation and the time it takes to annotated sentiment lexicons.
Though the PPMI based Amharic Sentiment lexicon outperforms the manual lexicon, prediction (word embedding) based approach is recommended to generate sentiment lexicon for Amharic language to handle context sensitive terms.

assistant: text: Corpus based Algorithm is developed generate Amharic Sentiment lexicon relying on corpus