This module allows an in-depth analysis of the “SBS_associations.csv” file produced by the main SBS analysis. Please upload the “SBS_associations.csv” file previously produced by the SBS analysis without changing its name or content (encoding must always be utf8).
Basic textual metrics are calculated at every run (such as counting the number of associations). In addition, you can choose to carry out these other analyses:
Dimensions: this field can be used to specify custom dimensions for the analysis of the brand image. You can use a dictionary to represent each dimension. The following syntax has to be used
"dimension_name1":["word1","word2",..], "dimension_name2":["word6","word8",..],... It is possible to repeat the same word in different dimensions. Please use the lowercase. The dimension name will not be considered as a word for the analysis. Additionally, asterisks can be used at the end of words, indicating that a specific word could be completed with any possible set of characters. For example, if the word
"asp*" is used, this will match both the words
"aspire". This also works with multiple words, for example
"financial sect*" will match the words
Language: this is the language used for the analyses described in the following.
Analyses: similarly to the Text Analysis function, you could choose to analyze the brand associations by considering:
Complexity: calculates the language complexity. The function provides several metrics: the number of words of six or more letters (absolute and relative frequencies), the average word length and 3 other complexity scores calculated by using the TF-IDF function and taking the word frequency distribution into account.
Emotions: calculates several dimensions of the language used (such as the degree of positive and negative emotions or the language orientation towards the past or future). Some dimensions might result empty, especially if you removed stop-words during the SBS analysis that produced the associations’ file.
Crovitz: calculates the frequencies of the Crovitz’s relational words. Some dimensions might result empty, especially if you removed stop-words during the SBS analysis that produced the associations file.
Sentiment is not an option here, as it is already calculated for each brand (and time interval) by the main SBS analysis (which has been used to produce the SBS_associations.csv file).
Three files are generated by the Image Explorer module:
AssoOverall: here, we get a summary of the main textual associations of each brand (up to 600), summing frequencies over time.
AssoDimensionsOverall: also in this case, we sum frequencies over time, but we consider the dimensions and analyses specified in the parameters. For each dimension, we get:
_totcount: the count of matching associations (sum of frequencies);
_rel: the count of matching associations divided by the total number of associations for that brand;
_reldim: the count of matching associations divided by the total number of associations for that dimension, with respect to the different brands.
AssoDimensionsByTime: here, we get the same results of
AssoDimensionsOverall, but for each time interval.
_reldimare calculated considering the total number of matching associations in a time interval and the total number of matching associations in a time interval with respect to the other brands.