The exponential growth of social media data in the era of Web 2.0 has necessitated advanced techniques for sentiment analysis. While sentiment analysis in monolingual datasets has received significant attention that in code-mixed datasets still need to be studied more. Code-mixed data often contain a mixture of monolingual content (might be in transliterated form), single-script but multilingual content, and multi-script multilingual content. This paper explores the issue from three important angles. What will be the best strategy to deal with the data for sentiment detection? Whether to train the classifier with the whole of the dataset or only with the pure code-mixed subset from the dataset? How much important is the language identification (LID) for the task? If LID is to be done, how, and when will it be used to yield the best performance? We explore the questions in the light of three datasets of Tamil–English, Kannada–English, and Malayalam–English YouTube social media comments. Our solution incorporated mBERT and an optional LID module. We report our results using a set of metrics like precision, recall, $F_1$ score, and accuracy. The solutions provide considerable performance gain and some interesting insights for sentiment analysis from code-mixed data.

This paper examines patterns in an Estonian–English bilingual child’s spontaneous speech, employing a computational application of the traceback method, which is used in usage-based linguistics. Forty-five hours of data were analyzed to check what proportion of patterns from code-mixed utterances are attested in the child’s monolingual data and in her input. Pattern overlap between the child’s and the caregivers’ speech was also examined. Results show that about one-third of code-mixed utterances can be traced back to the child’s input and one-third also to her own monolingual data. A little over half of the child’s utterances are either chunks or frame-and-slot patterns from the caregivers’ speech. These results make it evident that the traceback method can also be applied to language pairs that are genealogically more distant, though limitations exist.

Word-level language identification is an essential prerequisite for extracting useful information from code-mixed social media content. Previous studies in word-level language identification show two important observations. First, the local context is an important indicator of the language of a word when a word is valid in multiple languages. Second, considering the word in isolation from its context leads to more effective language classification when a word is borrowed or embedded into sentences of other languages. In this paper, we propose a framework for language identification that makes use of a dynamic switching mechanism for effective language classification of both words that are borrowed or embedded from other languages as well as words that are valid in multiple languages. For a given input, the proposed switching mechanism makes a dynamic decision to bias its prediction either towards the prediction obtained by the contextual information or that obtained by the word in isolation. In contrast to existing studies that rely upon large amounts of annotated data for robust performance in a multilingual environment, the proposed approach uses minimal annotated resources and no external resources, making it easily extendible to newer languages. Evaluation over a corpus of transliterated Facebook comments shows that the proposed approach outperforms its baseline counterparts: classification based on the contextual information, classification based on the word in isolation, as well as an ensemble of the two classifiers.

Although there is a body of work investigating code-switching (alternation between two languages in production) in the preschool period, it largely relies on case studies or very small samples. The current work seeks to extend extant research by exploring the development of code-switching longitudinally from 31 to 39 months of age in two distinct groups of bilingual children: Spanish–English children in San Diego and French–English children in Montréal. In two studies, consistent with previous research, children code-switched more often between than within utterances and code-switched more content than function words. Additionally, children code-switched more from Spanish or French to English than the reverse. Importantly, the factors driving the rate of code-switching differed across samples such that exposure was the most important predictor of code-switching in Spanish–English children whereas proficiency was the more important predictor in French–English children.

In this study we followed the characteristics and use of code-mixing by eight KODAs – hearing children of Deaf parents – from the age of 12 to 36 months. The children's interaction was video-recorded twice a year during three different play sessions: with their Deaf parent, with the Deaf parent and a hearing adult, and with the hearing adult alone. Additionally, data were collected on the children's overall language development in both sign language and spoken language. Our results showed that the children preferred to produce code-blends – simultaneous production of semantically congruent signs and words – in a way that was in accordance with the morphosyntactic structure of both languages being acquired. A Deaf parent as the interlocutor increased the number of and affected the type of code-blended utterances. These findings suggest that code-mixing in young bimodal bilingual KODA children can be highly systematic and synchronised in nature and can indicate pragmatic development.

Tariana is spoken by about 100 people in the multilingual area of the Vaupés basin in northwest Amazonia (Brazil). Other languages spoken in the area are members of the East Tucanoan subgroup, with its most numerous representative, the Tucano language, rapidly gaining ground as a lingua franca. Also spoken are Makú languages; Baniwa, an Arawak language spoken on the fringes of the area and closely related to Tariana; and Portuguese, the national language. The area is known for its language group exogamy and institutionalized multilingualism, with its language being the badge of identity for each group. Language choice is motivated by power relationship and by status, and there are strict rules for code-switching. Inserting bits of other languages while speaking Tariana (“code-mixing”) has different consequences that mirror existing ethnic stereotypes. Code-mixing with Tucano is considered a “language violation”; using elements of Baniwa is considered funny, while mixing different Tariana dialects implies that one “cannot speak Tariana properly.” Overusing Portuguese is associated with the negative image of an Indian who tries to be better than his peers.