This work investigates empirically the impact of political party control over its candidates or vice versa on winning an election using a Natural Language Processing (NLP) technique called Sentiment Analysis (SA). To do this, a set of 7430 tweets bearing or related to #AnambraDecides2017 was streamed during the November 18, 2017 Anambra State gubernatorial election. These are Twitter discussions on the top 5 political parties and their candidates termed political actors in this paper. We conduct polarity and subjectivity sentiment analyses on all the tweets considering time as a useful dimension of SA. Furthermore, we use the word frequency to find words most associated to the political actors in a given time. We find most talked about topics using a topic modeling algorithm and how the computed sentiments and most frequent words are related to the topics per political actor. Among other things, we deduced from the experimental results that even though a political party serves as a platform that sales the personality of a candidate, the acceptance of the candidate/party adds to the winning of an election. For example, we found the winner of the election Willie Obiano benefiting from the values his party share among the people of the State. Associating his name with his party All Progressive Grand Alliance (APGA) displays more positive sentiments and the Subjective Sentiment Analysis indicates that Twitter users mentioning APGA are less emotionally subjective in their tweets than the other parties.

Abstract. Igbo is a low-resource African language with orthographic and tonal diacritics, which capture distinctions between words that are important for both meaning and pronunciation, and hence of potential value for a range of language processing tasks. Such diacritics, however, are often largely absent from the electronic texts we might want to process, or assemble into corpora, and so the need arises for effective methods for automatic diacritic restoration for Igbo. In this paper, we experiment using an Igbo bible corpus, which is extensively marked for vowel distinctions, and partially for tonal distinctions, and attempt the task of reinstating these diacritics when they have been deleted. We investigate a number of word-level diacritic restoration methods, based on n-grams, under a closed-world assumption, achieving an accuracy of 98.83% with our most effective method.

This project aims to develop linguistic resources to support computational NLP research on the Igbo language. The starting point for this project is the development of a new part-of-speech tagging scheme based on the EAGLES tagset guidelines, adapted to incorporate additional language internal features. The tags are currently being used in a part-of-speech annotation task for the development of POS tagged Igbo corpus. The proposed tagset has 59 tags.

Properly written texts in Igbo, a low resource African language, are rich in both orthographic and tonal diacritics. Diacritics are essential in capturing the distinctions in pronunciation and meaning of words, as well as in lexical disambiguation. Unfortunately, most electronic texts in diacritic languages are written without diacritics. This makes diacritic restoration a necessary step in corpus building and language processing tasks for languages with diacritics. In our previous work, we built some n−gram models with simple smoothing techniques based on a closedworld assumption. However, as a classification task, diacritic restoration is well suited for and will be more generalisable with machine learning. This paper, therefore, presents a more standard approach to dealing with the task which involves the application of machine learning algorithms.

Automatic multiple-choice question generation (MCQG) is a useful yet challenging task in Natural Language Processing (NLP). It is the task of automatic generation of correct and relevant questions from textual data. Despite its usefulness, manually creating sizeable, meaningful and relevant questions is a time-consuming and challenging task for teachers. In this paper, we present an NLP-based system for automatic MCQG for Computer-Based Testing Examination (CBTE).We used NLP technique to extract keywords that are important words in a given lesson material. To validate that the system is not perverse, five lesson materials were used to check the effectiveness and efficiency of the system. The manually extracted keywords by the teacher were compared to the auto-generated keywords and the result shows that the system was capable of extracting keywords from lesson materials in setting examinable questions. This outcome is presented in a user-friendly interface for easy accessibility.